From 6b9a2c9324fa9daf56631149e326e5025ff073bc Mon Sep 17 00:00:00 2001 From: Steve Heim Date: Sat, 16 Mar 2024 12:04:58 -0400 Subject: [PATCH 01/77] add coupling to humanoid torques --- gym/envs/mit_humanoid/mit_humanoid.py | 30 +++++++++----------- gym/envs/mit_humanoid/mit_humanoid_config.py | 2 +- 2 files changed, 14 insertions(+), 18 deletions(-) diff --git a/gym/envs/mit_humanoid/mit_humanoid.py b/gym/envs/mit_humanoid/mit_humanoid.py index 74e35d5a..10169afc 100644 --- a/gym/envs/mit_humanoid/mit_humanoid.py +++ b/gym/envs/mit_humanoid/mit_humanoid.py @@ -12,23 +12,19 @@ def _init_buffers(self): super()._init_buffers() def _compute_torques(self): - self.desired_pos_target = self.dof_pos_target + self.default_dof_pos - q = self.dof_pos.clone() - qd = self.dof_vel.clone() - q_des = self.desired_pos_target.clone() - qd_des = self.dof_vel_target.clone() - tau_ff = self.tau_ff.clone() - kp = self.p_gains.clone() - kd = self.d_gains.clone() - - if self.cfg.asset.apply_humanoid_jacobian: - torques = _apply_coupling(q, qd, q_des, qd_des, kp, kd, tau_ff) - else: - torques = kp * (q_des - q) + kd * (qd_des - qd) + tau_ff - - torques = torch.clip(torques, -self.torque_limits, self.torque_limits) - - return torques.view(self.torques.shape) + return torch.clip( + _apply_coupling( + self.dof_pos, + self.dof_vel, + self.dof_pos_target + self.default_dof_pos, + self.dof_vel_target, + self.p_gains, + self.d_gains, + self.tau_ff, + ), + -self.torque_limits, + self.torque_limits, + ) # view shape def _reward_lin_vel_z(self): # Penalize z axis base linear velocity w. squared exp diff --git a/gym/envs/mit_humanoid/mit_humanoid_config.py b/gym/envs/mit_humanoid/mit_humanoid_config.py index 3439e58c..cdc4334f 100644 --- a/gym/envs/mit_humanoid/mit_humanoid_config.py +++ b/gym/envs/mit_humanoid/mit_humanoid_config.py @@ -248,7 +248,7 @@ class algorithm(LeggedRobotRunnerCfg.algorithm): class runner(LeggedRobotRunnerCfg.runner): policy_class_name = "ActorCritic" - algorithm_class_name = "PPO" + algorithm_class_name = "PPO2" num_steps_per_env = 24 max_iterations = 1000 run_name = "Standing" From e52208f5527c99849938726841ebba87a4dd01f6 Mon Sep 17 00:00:00 2001 From: Steve Heim Date: Sat, 16 Mar 2024 12:14:47 -0400 Subject: [PATCH 02/77] add exp avg filtering --- gym/envs/mit_humanoid/mit_humanoid.py | 25 ++++++++++---------- gym/envs/mit_humanoid/mit_humanoid_config.py | 2 ++ 2 files changed, 14 insertions(+), 13 deletions(-) diff --git a/gym/envs/mit_humanoid/mit_humanoid.py b/gym/envs/mit_humanoid/mit_humanoid.py index 10169afc..67b21868 100644 --- a/gym/envs/mit_humanoid/mit_humanoid.py +++ b/gym/envs/mit_humanoid/mit_humanoid.py @@ -2,6 +2,7 @@ from gym.envs.base.legged_robot import LeggedRobot from .jacobian import _apply_coupling +from gym.utils import exp_avg_filter class MIT_Humanoid(LeggedRobot): @@ -12,19 +13,17 @@ def _init_buffers(self): super()._init_buffers() def _compute_torques(self): - return torch.clip( - _apply_coupling( - self.dof_pos, - self.dof_vel, - self.dof_pos_target + self.default_dof_pos, - self.dof_vel_target, - self.p_gains, - self.d_gains, - self.tau_ff, - ), - -self.torque_limits, - self.torque_limits, - ) # view shape + torques = _apply_coupling( + self.dof_pos, + self.dof_vel, + self.dof_pos_target + self.default_dof_pos, + self.dof_vel_target, + self.p_gains, + self.d_gains, + self.tau_ff, + ) + torques = torques.clip(-self.torque_limits, self.torque_limits) + return exp_avg_filter(torques, self.torques, self.cfg.control.filter_gain) def _reward_lin_vel_z(self): # Penalize z axis base linear velocity w. squared exp diff --git a/gym/envs/mit_humanoid/mit_humanoid_config.py b/gym/envs/mit_humanoid/mit_humanoid_config.py index cdc4334f..d442216d 100644 --- a/gym/envs/mit_humanoid/mit_humanoid_config.py +++ b/gym/envs/mit_humanoid/mit_humanoid_config.py @@ -111,6 +111,8 @@ class control(LeggedRobotCfg.control): ctrl_frequency = 100 desired_sim_frequency = 800 + filter_gain = 0.1586 # 1: no filtering, 0: wall + class commands: resampling_time = 10.0 # time before command are changed[s] From 54b826db9475061b01139dc58b6d0e0d930dedb4 Mon Sep 17 00:00:00 2001 From: Steve Heim Date: Sat, 16 Mar 2024 19:00:50 -0400 Subject: [PATCH 03/77] add oscillators and phase-based rewards, some tuning --- gym/envs/base/legged_robot.py | 45 +- gym/envs/base/legged_robot_config.py | 2 +- .../mit_humanoid/humanoid_running_config.py | 2 +- gym/envs/mit_humanoid/mit_humanoid.py | 91 ++ gym/envs/mit_humanoid/mit_humanoid_config.py | 157 ++- .../robots/mit_humanoid/friction_model_L.pt | Bin 0 -> 13519 bytes .../robots/mit_humanoid/friction_model_R.pt | Bin 0 -> 13519 bytes .../mit_humanoid/urdf/humanoid_F_sf.urdf | 16 +- .../urdf/humanoid_F_sf_learnt.urdf | 996 ++++++++++++++++++ 9 files changed, 1242 insertions(+), 67 deletions(-) create mode 100644 resources/robots/mit_humanoid/friction_model_L.pt create mode 100644 resources/robots/mit_humanoid/friction_model_R.pt create mode 100644 resources/robots/mit_humanoid/urdf/humanoid_F_sf_learnt.urdf diff --git a/gym/envs/base/legged_robot.py b/gym/envs/base/legged_robot.py index e02507d5..1bc4623e 100644 --- a/gym/envs/base/legged_robot.py +++ b/gym/envs/base/legged_robot.py @@ -532,6 +532,29 @@ def _init_buffers(self): self.num_envs, 1, dtype=torch.float, device=self.device ) + # # * get the body_name to body_index dict + # body_dict = self.gym.get_actor_rigid_body_dict( + # self.envs[0], self.actor_handles[0] + # ) + # # * extract a list of body_names where the index is the id number + # body_names = [ + # body_tuple[0] + # for body_tuple in sorted( + # body_dict.items(), key=lambda body_tuple: body_tuple[1] + # ) + # ] + # # * construct a list of id numbers corresponding to end_effectors + # self.end_effector_ids = [] + # for end_effector_name in self.cfg.asset.foot_collisionbox_names: + # self.end_effector_ids.extend( + # [ + # body_names.index(body_name) + # for body_name in body_names + # if end_effector_name in body_name + # ] + # ) + # # ---------------------------------------- + if self.cfg.terrain.measure_heights: self.height_points = self._init_height_points() self.measured_heights = 0 @@ -994,11 +1017,11 @@ def _reward_lin_vel_z(self): def _reward_ang_vel_xy(self): """Penalize xy axes base angular velocity""" - return -torch.sum(torch.square(self.base_ang_vel[:, :2]), dim=1) + return -torch.mean(torch.square(self.base_ang_vel[:, :2]), dim=1) def _reward_orientation(self): """Penalize non flat base orientation""" - return -torch.sum(torch.square(self.projected_gravity[:, :2]), dim=1) + return -torch.mean(torch.square(self.projected_gravity[:, :2]), dim=1) def _reward_base_height(self): """Penalize base height away from target""" @@ -1009,11 +1032,11 @@ def _reward_base_height(self): def _reward_torques(self): """Penalize torques""" - return -torch.sum(torch.square(self.torques), dim=1) + return -torch.mean(torch.square(self.torques), dim=1) def _reward_dof_vel(self): """Penalize dof velocities""" - return -torch.sum(torch.square(self.dof_vel), dim=1) + return -torch.mean(torch.square(self.dof_vel), dim=1) def _reward_action_rate(self): """Penalize changes in actions""" @@ -1025,7 +1048,7 @@ def _reward_action_rate(self): ) / dt2 ) - return -torch.sum(error, dim=1) + return -torch.mean(error, dim=1) def _reward_action_rate2(self): """Penalize changes in actions""" @@ -1039,7 +1062,7 @@ def _reward_action_rate2(self): ) / dt2 ) - return -torch.sum(error, dim=1) + return -torch.mean(error, dim=1) def _reward_collision(self): """Penalize collisions on selected bodies""" @@ -1064,26 +1087,26 @@ def _reward_dof_pos_limits(self): # * lower limit out_of_limits = -(self.dof_pos - self.dof_pos_limits[:, 0]).clip(max=0.0) out_of_limits += (self.dof_pos - self.dof_pos_limits[:, 1]).clip(min=0.0) - return -torch.sum(out_of_limits, dim=1) + return -torch.mean(out_of_limits, dim=1) def _reward_dof_vel_limits(self): """Penalize dof velocities too close to the limit""" # * clip to max error = 1 rad/s per joint to avoid huge penalties limit = self.cfg.reward_settings.soft_dof_vel_limit error = self.dof_vel.abs() - self.dof_vel_limits * limit - return -torch.sum(error.clip(min=0.0, max=1.0), dim=1) + return -torch.mean(error.clip(min=0.0, max=1.0), dim=1) def _reward_torque_limits(self): """penalize torques too close to the limit""" limit = self.cfg.reward_settings.soft_torque_limit error = self.torques.abs() - self.torque_limits * limit - return -torch.sum(error.clip(min=0.0, max=1.0), dim=1) + return -torch.mean(error.clip(min=0.0, max=1.0), dim=1) def _reward_tracking_lin_vel(self): """Tracking of linear velocity commands (xy axes)""" error = torch.square(self.commands[:, :2] - self.base_lin_vel[:, :2]) error = torch.exp(-error / self.cfg.reward_settings.tracking_sigma) - return torch.sum(error, dim=1) + return torch.mean(error, dim=1) def _reward_tracking_ang_vel(self): """Tracking of angular velocity commands (yaw)""" @@ -1092,7 +1115,7 @@ def _reward_tracking_ang_vel(self): def _reward_feet_contact_forces(self): """penalize high contact forces""" - return -torch.sum( + return -torch.mean( ( torch.norm(self.contact_forces[:, self.feet_indices, :], dim=-1) - self.cfg.reward_settings.max_contact_force diff --git a/gym/envs/base/legged_robot_config.py b/gym/envs/base/legged_robot_config.py index 2955f5a5..221a6afc 100644 --- a/gym/envs/base/legged_robot_config.py +++ b/gym/envs/base/legged_robot_config.py @@ -163,7 +163,7 @@ class asset: file = "" # * name of the feet bodies, # * used to index body state and contact force tensors - foot_name = "None" + foot_name = "foot" penalize_contacts_on = [] terminate_after_contacts_on = [] end_effector_names = [] diff --git a/gym/envs/mit_humanoid/humanoid_running_config.py b/gym/envs/mit_humanoid/humanoid_running_config.py index 86a113f4..fd21c9b4 100644 --- a/gym/envs/mit_humanoid/humanoid_running_config.py +++ b/gym/envs/mit_humanoid/humanoid_running_config.py @@ -142,7 +142,7 @@ class asset(LeggedRobotCfg.asset): # +'humanoid_fixed_arms_full.urdf') file = ( "{LEGGED_GYM_ROOT_DIR}/resources/robots/" - + "mit_humanoid/urdf/humanoid_F_sf.urdf" + + "mit_humanoid/urdf/humanoid_F_sf_learnt.urdf" ) keypoints = ["base"] end_effectors = ["left_foot", "right_foot"] diff --git a/gym/envs/mit_humanoid/mit_humanoid.py b/gym/envs/mit_humanoid/mit_humanoid.py index 67b21868..17167286 100644 --- a/gym/envs/mit_humanoid/mit_humanoid.py +++ b/gym/envs/mit_humanoid/mit_humanoid.py @@ -11,7 +11,29 @@ def __init__(self, gym, sim, cfg, sim_params, sim_device, headless): def _init_buffers(self): super()._init_buffers() + self.oscillators = torch.zeros(self.num_envs, 2, device=self.device) + self.oscillator_obs = torch.zeros(self.num_envs, 4, device=self.device) + self.oscillator_freq = torch.zeros(self.num_envs, 2, device=self.device) + def _reset_system(self, env_ids): + if len(env_ids) == 0: + return + super()._reset_system(env_ids) + # reset oscillators, with a pi phase shift between left and right + self.oscillators[env_ids, 0] = ( + torch.rand(len(env_ids), device=self.device) * 2 * torch.pi + ) + self.oscillators[env_ids, 1] = self.oscillators[env_ids, 0] + torch.pi + self.oscillators = torch.remainder(self.oscillators, 2 * torch.pi) + # reset oscillator velocities to base freq + self.oscillator_freq[env_ids] = self.cfg.oscillator.base_frequency + # recompute oscillator observations + self.oscillator_obs = torch.cat( + (self.oscillators.cos(), self.oscillators.sin()), dim=1 + ) + return + + # compute_torques accounting for coupling, and filtering torques def _compute_torques(self): torques = _apply_coupling( self.dof_pos, @@ -25,6 +47,44 @@ def _compute_torques(self): torques = torques.clip(-self.torque_limits, self.torque_limits) return exp_avg_filter(torques, self.torques, self.cfg.control.filter_gain) + # oscillator integration + + def _post_decimation_step(self): + super()._post_decimation_step() + self._step_oscillators() + + def _step_oscillators(self, dt=None): + if dt is None: + dt = self.dt + self.oscillators += (self.oscillator_freq * 2 * torch.pi) * dt + self.oscillators = torch.remainder(self.oscillators, 2 * torch.pi) + self.oscillator_obs = torch.cat( + (torch.cos(self.oscillators), torch.sin(self.oscillators)), dim=1 + ) + + # --- rewards --- + + def _switch(self, mode=None): + c_vel = torch.linalg.norm(self.commands, dim=1) + switch = torch.exp( + -torch.square( + torch.max( + torch.zeros_like(c_vel), + c_vel - self.cfg.reward_settings.switch_scale, + ) + ) + / self.cfg.reward_settings.switch_scale + ) + if mode is None or mode == "stand": + return switch + elif mode == "move": + return 1 - switch + + def _reward_lin_vel_xy(self): + return torch.exp( + -torch.linalg.norm(self.commands[:, :2] - self.base_lin_vel[:, :2], dim=1) + ) + def _reward_lin_vel_z(self): # Penalize z axis base linear velocity w. squared exp return self._sqrdexp(self.base_lin_vel[:, 2] / self.scales["base_lin_vel"]) @@ -52,6 +112,7 @@ def _reward_tracking_lin_vel(self): def _reward_dof_vel(self): # Penalize dof velocities return torch.mean(self._sqrdexp(self.dof_vel / self.scales["dof_vel"]), dim=1) + # * self._switch("stand") def _reward_dof_near_home(self): return torch.mean( @@ -60,3 +121,33 @@ def _reward_dof_near_home(self): ), dim=1, ) + + def _reward_stand_still(self): + """Penalize motion at zero commands""" + # * normalize angles so we care about being within 5 deg + rew_pos = torch.mean( + self._sqrdexp((self.dof_pos - self.default_dof_pos) / torch.pi * 36), dim=1 + ) + rew_vel = torch.mean(self._sqrdexp(self.dof_vel), dim=1) + rew_base_vel = torch.mean(torch.square(self.base_lin_vel), dim=1) + rew_base_vel += torch.mean(torch.square(self.base_ang_vel), dim=1) + return rew_vel + rew_pos - rew_base_vel # * self._switch("stand") + + def _reward_stance(self): + phase = torch.maximum( + torch.zeros_like(self.oscillators), -self.oscillators.sin() + ) # positive during swing, negative during stance + return (phase * self._compute_grf()).mean(dim=1) + + def _reward_swing(self): + phase = torch.maximum( + torch.zeros_like(self.oscillators), self.oscillators.sin() + ) # positive during swing, negative during stance + return -(phase * self._compute_grf()).mean(dim=1) + + def _compute_grf(self, grf_norm=True): + grf = torch.norm(self.contact_forces[:, self.feet_indices, :], dim=-1) + if grf_norm: + return torch.clamp_max(grf / self.cfg.asset.total_mass, 1.0) + else: + return grf diff --git a/gym/envs/mit_humanoid/mit_humanoid_config.py b/gym/envs/mit_humanoid/mit_humanoid_config.py index d442216d..826480e7 100644 --- a/gym/envs/mit_humanoid/mit_humanoid_config.py +++ b/gym/envs/mit_humanoid/mit_humanoid_config.py @@ -3,13 +3,15 @@ LeggedRobotRunnerCfg, ) +BASE_HEIGHT_REF = 0.80 + class MITHumanoidCfg(LeggedRobotCfg): class env(LeggedRobotCfg.env): num_envs = 4096 num_observations = 49 + 3 * 18 # 121 num_actuators = 18 - episode_length_s = 100 # episode length in seconds + episode_length_s = 5 # episode length in seconds num_privileged_obs = num_observations class terrain(LeggedRobotCfg.terrain): @@ -75,13 +77,13 @@ class init_state(LeggedRobotCfg.init_state): ] # yaw root_vel_range = [ - [-0.1, 0.1], # x - [-0.1, 0.1], # y - [-0.1, 0.1], # z - [-0.1, 0.1], # roll - [-0.1, 0.1], # pitch - [-0.1, 0.1], - ] # yaw + [-0.5, 1.5], # x + [-0.55, 0.55], # y + [-0.35, 0.1], # z + [-0.35, 0.35], # roll + [-0.35, 0.35], # pitch + [-0.35, 0.35], # yaw + ] class control(LeggedRobotCfg.control): # * PD Drive parameters: @@ -97,22 +99,25 @@ class control(LeggedRobotCfg.control): "elbow": 40.0, } # [N*m/rad] damping = { - "hip_yaw": 5.0, - "hip_abad": 5.0, - "hip_pitch": 5.0, - "knee": 5.0, - "ankle": 5.0, - "shoulder_pitch": 5.0, - "shoulder_abad": 5.0, - "shoulder_yaw": 5.0, - "elbow": 5.0, + "hip_yaw": 2.0, + "hip_abad": 2.0, + "hip_pitch": 2.0, + "knee": 2.0, + "ankle": 2.0, + "shoulder_pitch": 2.0, + "shoulder_abad": 2.0, + "shoulder_yaw": 2.0, + "elbow": 1.0, } # [N*m*s/rad] ctrl_frequency = 100 - desired_sim_frequency = 800 + desired_sim_frequency = 1000 filter_gain = 0.1586 # 1: no filtering, 0: wall + class oscillator: + base_frequency = 1.5 # [Hz] + class commands: resampling_time = 10.0 # time before command are changed[s] @@ -138,6 +143,7 @@ class asset(LeggedRobotCfg.asset): "{LEGGED_GYM_ROOT_DIR}/resources/robots/" + "mit_humanoid/urdf/humanoid_R_sf.urdf" ) + # foot_collisionbox_names = ["foot"] foot_name = "foot" penalize_contacts_on = ["base", "arm"] terminate_after_contacts_on = ["base"] @@ -148,35 +154,88 @@ class asset(LeggedRobotCfg.asset): # * see GymDofDriveModeFlags # * (0 is none, 1 is pos tgt, 2 is vel tgt, 3 effort) default_dof_drive_mode = 3 + fix_base_link = False disable_gravity = False disable_motors = False apply_humanoid_jacobian = False + total_mass = 25.0 class reward_settings(LeggedRobotCfg.reward_settings): soft_dof_pos_limit = 0.9 soft_dof_vel_limit = 0.9 soft_torque_limit = 0.9 max_contact_force = 1500.0 - - base_height_target = 0.65 + base_height_target = BASE_HEIGHT_REF tracking_sigma = 0.5 + # a smooth switch based on |cmd| (commanded velocity). + switch_scale = 0.5 + switch_threshold = 0.2 + class scaling(LeggedRobotCfg.scaling): # * dimensionless time: sqrt(L/g) or sqrt(I/[mgL]), with I=I0+mL^2 - virtual_leg_length = 0.65 - dimensionless_time = (virtual_leg_length / 9.81) ** 0.5 - base_height = virtual_leg_length - base_lin_vel = virtual_leg_length / dimensionless_time - base_ang_vel = 3.14 / dimensionless_time - dof_vel = 20 # ought to be roughly max expected speed. - height_measurements = virtual_leg_length - - # todo check order of joints, create per-joint scaling - dof_pos = 3.14 + # virtual_leg_length = 0.65 + # dimensionless_time = (virtual_leg_length / 9.81) ** 0.5 + # base_height = virtual_leg_length + # base_lin_vel = virtual_leg_length / dimensionless_time + # base_ang_vel = 3.14 / dimensionless_time + # dof_vel = 20 # ought to be roughly max expected speed. + # height_measurements = virtual_leg_length + + # # todo check order of joints, create per-joint scaling + # dof_pos = 3.14 + # dof_pos_obs = dof_pos + # # * Action scales + # dof_pos_target = dof_pos + # tau_ff = 0.1 + base_ang_vel = 2.5 + base_lin_vel = 1.5 + commands = 1 + base_height = BASE_HEIGHT_REF + dof_pos = [ + 0.1, + 0.2, + 0.4, + 0.4, + 0.4, + 0.1, + 0.2, + 0.4, + 0.4, + 0.4, + 0.5, + 0.5, + 0.5, + 0.5, + 0.5, + 0.5, + 0.5, + 0.5, + ] dof_pos_obs = dof_pos - # * Action scales + # # * Action scales dof_pos_target = dof_pos - tau_ff = 0.1 + dof_vel = [ + 0.5, + 1.0, + 4.0, + 4.0, + 2.0, + 0.5, + 1.0, + 4.0, + 4.0, + 2.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + ] + dof_pos_history = 3 * dof_pos_obs class MITHumanoidRunnerCfg(LeggedRobotRunnerCfg): @@ -184,6 +243,7 @@ class MITHumanoidRunnerCfg(LeggedRobotRunnerCfg): runner_class_name = "OnPolicyRunner" class policy(LeggedRobotRunnerCfg.policy): + disable_actions = False init_noise_std = 1.0 actor_hidden_dims = [512, 256, 128] critic_hidden_dims = [512, 256, 128] @@ -199,26 +259,27 @@ class policy(LeggedRobotRunnerCfg.policy): "dof_pos_obs", "dof_vel", "dof_pos_history", + "oscillator_obs", ] critic_obs = actor_obs actions = ["dof_pos_target"] class noise: - base_height = 0.05 - dof_pos_obs = 0.0 - dof_vel = 0.0 - base_lin_vel = 0.1 - base_ang_vel = 0.2 - projected_gravity = 0.05 - height_measurements = 0.1 + base_height = 0.0 # 0.05 + dof_pos_obs = 0.0 # 0.0 + dof_vel = 0.0 # 0.0 + base_lin_vel = 0.0 # 0.1 + base_ang_vel = 0.0 # 0.2 + projected_gravity = 0.0 # 0.05 + height_measurements = 0.0 # 0.1 class reward: class weights: - tracking_ang_vel = 0.5 - tracking_lin_vel = 0.5 - orientation = 1.5 - torques = 5.0e-6 + tracking_ang_vel = 1.5 + tracking_lin_vel = 3.0 + orientation = 1.0 + torques = 5.0e-4 min_base_height = 1.5 action_rate = 0.01 action_rate2 = 0.001 @@ -226,8 +287,10 @@ class weights: ang_vel_xy = 0.0 dof_vel = 0.0 stand_still = 0.0 - dof_pos_limits = 0.0 - dof_near_home = 0.5 + dof_pos_limits = 0.25 + dof_near_home = 0.1 + stance = 1.0 + swing = 1.0 class termination_weight: termination = 15 @@ -245,13 +308,15 @@ class algorithm(LeggedRobotRunnerCfg.algorithm): schedule = "adaptive" # could be adaptive, fixed gamma = 0.999 lam = 0.95 + discount_horizon = 0.5 # [s] + GAE_bootstrap_horizon = 0.2 # [s] desired_kl = 0.01 max_grad_norm = 1.0 class runner(LeggedRobotRunnerCfg.runner): policy_class_name = "ActorCritic" algorithm_class_name = "PPO2" - num_steps_per_env = 24 + num_steps_per_env = 32 max_iterations = 1000 run_name = "Standing" experiment_name = "Humanoid" diff --git a/resources/robots/mit_humanoid/friction_model_L.pt b/resources/robots/mit_humanoid/friction_model_L.pt new file mode 100644 index 0000000000000000000000000000000000000000..d1d680d5aa88e8f496d957fa7db1640c98ee00e1 GIT binary patch literal 13519 zcmbVz2{={X+qQX56j4GlCQ;_$>}MU4%A86nl1Mah(4KQDOb8#{Mg!LYGF$_(v>Uup)G6kbl&|<&j|# z{wqyoc%lXdK9c>Q$o|NHMGJXizaTPBOTz-9rvKU~!xQ%r?vEMIlkkz~uLD;E1=x<~ 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a/resources/robots/mit_humanoid/urdf/humanoid_F_sf.urdf +++ b/resources/robots/mit_humanoid/urdf/humanoid_F_sf.urdf @@ -562,7 +562,7 @@ Simple Foot: foot approximated as single box-contact --> + type="fixed"> @@ -607,7 +607,7 @@ Simple Foot: foot approximated as single box-contact --> + type="fixed"> @@ -660,7 +660,7 @@ Simple Foot: foot approximated as single box-contact --> + type="fixed"> @@ -711,7 +711,7 @@ Simple Foot: foot approximated as single box-contact --> + type="fixed"> @@ -790,7 +790,7 @@ Simple Foot: foot approximated as single box-contact --> + type="fixed"> @@ -835,7 +835,7 @@ Simple Foot: foot approximated as single box-contact --> + type="fixed"> @@ -888,7 +888,7 @@ Simple Foot: foot approximated as single box-contact --> + type="fixed"> @@ -939,7 +939,7 @@ Simple Foot: foot approximated as single box-contact --> + type="fixed"> diff --git a/resources/robots/mit_humanoid/urdf/humanoid_F_sf_learnt.urdf b/resources/robots/mit_humanoid/urdf/humanoid_F_sf_learnt.urdf new file mode 100644 index 00000000..5e76f34a --- /dev/null +++ b/resources/robots/mit_humanoid/urdf/humanoid_F_sf_learnt.urdf @@ -0,0 +1,996 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + From c9411cdf0c07b55d7095b07233abbf41f5eeb5c0 Mon Sep 17 00:00:00 2001 From: Steve Heim Date: Mon, 18 Mar 2024 14:04:09 -0400 Subject: [PATCH 04/77] some tuning --- gym/envs/mit_humanoid/mit_humanoid.py | 17 ++++- gym/envs/mit_humanoid/mit_humanoid_config.py | 72 ++++++++++---------- 2 files changed, 49 insertions(+), 40 deletions(-) diff --git a/gym/envs/mit_humanoid/mit_humanoid.py b/gym/envs/mit_humanoid/mit_humanoid.py index 17167286..684f4cb3 100644 --- a/gym/envs/mit_humanoid/mit_humanoid.py +++ b/gym/envs/mit_humanoid/mit_humanoid.py @@ -111,8 +111,9 @@ def _reward_tracking_lin_vel(self): def _reward_dof_vel(self): # Penalize dof velocities - return torch.mean(self._sqrdexp(self.dof_vel / self.scales["dof_vel"]), dim=1) - # * self._switch("stand") + return torch.mean( + self._sqrdexp(self.dof_vel / self.scales["dof_vel"]), dim=1 + ) * self._switch("stand") def _reward_dof_near_home(self): return torch.mean( @@ -131,7 +132,7 @@ def _reward_stand_still(self): rew_vel = torch.mean(self._sqrdexp(self.dof_vel), dim=1) rew_base_vel = torch.mean(torch.square(self.base_lin_vel), dim=1) rew_base_vel += torch.mean(torch.square(self.base_ang_vel), dim=1) - return rew_vel + rew_pos - rew_base_vel # * self._switch("stand") + return rew_vel + rew_pos - rew_base_vel * self._switch("stand") def _reward_stance(self): phase = torch.maximum( @@ -151,3 +152,13 @@ def _compute_grf(self, grf_norm=True): return torch.clamp_max(grf / self.cfg.asset.total_mass, 1.0) else: return grf + + def _reward_walk_freq(self): + # Penalize deviation from base frequency + return torch.mean( + self._sqrdexp( + (self.oscillator_freq - self.cfg.oscillator.base_frequency) + / self.cfg.oscillator.base_frequency + ), + dim=1, + ) * self._switch("move") diff --git a/gym/envs/mit_humanoid/mit_humanoid_config.py b/gym/envs/mit_humanoid/mit_humanoid_config.py index 826480e7..7a8680e8 100644 --- a/gym/envs/mit_humanoid/mit_humanoid_config.py +++ b/gym/envs/mit_humanoid/mit_humanoid_config.py @@ -70,15 +70,15 @@ class init_state(LeggedRobotCfg.init_state): root_pos_range = [ [0.0, 0.0], # x [0.0, 0.0], # y - [0.7, 0.72], # z + [0.7, 0.8], # z [-0.1, 0.1], # roll [-0.1, 0.1], # pitch [-0.1, 0.1], ] # yaw root_vel_range = [ - [-0.5, 1.5], # x - [-0.55, 0.55], # y + [-0.5, 2.5], # x + [-0.5, 0.5], # y [-0.35, 0.1], # z [-0.35, 0.35], # roll [-0.35, 0.35], # pitch @@ -122,18 +122,18 @@ class commands: resampling_time = 10.0 # time before command are changed[s] class ranges: - lin_vel_x = [-1.0, 1.0] # min max [m/s] + lin_vel_x = [-1.0, 4.0] # min max [m/s] lin_vel_y = 1.0 # max [m/s] yaw_vel = 1 # max [rad/s] class push_robots: toggle = False - interval_s = 15 - max_push_vel_xy = 0.05 - push_box_dims = [0.1, 0.2, 0.3] # x,y,z [m] + interval_s = 2 + max_push_vel_xy = 0.6 + push_box_dims = [0.1, 0.1, 0.3] # x,y,z [m] class domain_rand: - randomize_friction = False + randomize_friction = True friction_range = [0.5, 1.25] randomize_base_mass = True added_mass_range = [-1.0, 1.0] @@ -157,7 +157,6 @@ class asset(LeggedRobotCfg.asset): fix_base_link = False disable_gravity = False disable_motors = False - apply_humanoid_jacobian = False total_mass = 25.0 class reward_settings(LeggedRobotCfg.reward_settings): @@ -195,22 +194,22 @@ class scaling(LeggedRobotCfg.scaling): dof_pos = [ 0.1, 0.2, - 0.4, - 0.4, - 0.4, + 0.8, + 0.8, + 0.8, 0.1, 0.2, - 0.4, - 0.4, - 0.4, - 0.5, - 0.5, - 0.5, - 0.5, - 0.5, - 0.5, - 0.5, - 0.5, + 0.8, + 0.8, + 0.8, + 0.1, + 0.1, + 0.1, + 0.1, + 0.1, + 0.1, + 0.1, + 0.1, ] dof_pos_obs = dof_pos # # * Action scales @@ -248,7 +247,7 @@ class policy(LeggedRobotRunnerCfg.policy): actor_hidden_dims = [512, 256, 128] critic_hidden_dims = [512, 256, 128] # * can be elu, relu, selu, crelu, lrelu, tanh, sigmoid - activation = "elu" + activation = "tanh" actor_obs = [ "base_height", @@ -266,13 +265,13 @@ class policy(LeggedRobotRunnerCfg.policy): actions = ["dof_pos_target"] class noise: - base_height = 0.0 # 0.05 - dof_pos_obs = 0.0 # 0.0 - dof_vel = 0.0 # 0.0 - base_lin_vel = 0.0 # 0.1 - base_ang_vel = 0.0 # 0.2 - projected_gravity = 0.0 # 0.05 - height_measurements = 0.0 # 0.1 + base_height = 0.05 + dof_pos_obs = 0.0 + dof_vel = 0.0 + base_lin_vel = 0.1 + base_ang_vel = 0.2 + projected_gravity = 0.05 + height_measurements = 0.1 class reward: class weights: @@ -285,12 +284,13 @@ class weights: action_rate2 = 0.001 lin_vel_z = 0.0 ang_vel_xy = 0.0 - dof_vel = 0.0 - stand_still = 0.0 + dof_vel = 0.25 + stand_still = 0.25 dof_pos_limits = 0.25 - dof_near_home = 0.1 + dof_near_home = 0.25 stance = 1.0 swing = 1.0 + walk_freq = 0.5 class termination_weight: termination = 15 @@ -300,7 +300,7 @@ class algorithm(LeggedRobotRunnerCfg.algorithm): value_loss_coef = 1.0 use_clipped_value_loss = True clip_param = 0.2 - entropy_coef = 0.001 + entropy_coef = 0.002 num_learning_epochs = 5 # * mini batch size = num_envs*nsteps / nminibatches num_mini_batches = 4 @@ -308,8 +308,6 @@ class algorithm(LeggedRobotRunnerCfg.algorithm): schedule = "adaptive" # could be adaptive, fixed gamma = 0.999 lam = 0.95 - discount_horizon = 0.5 # [s] - GAE_bootstrap_horizon = 0.2 # [s] desired_kl = 0.01 max_grad_norm = 1.0 From 90ff50cc34ba6a41f442a3c732e3c53dd1c600d5 Mon Sep 17 00:00:00 2001 From: Steve Heim Date: Mon, 18 Mar 2024 19:57:46 -0400 Subject: [PATCH 05/77] implement history with (approximated) arbitrary sampling frequency and history length. Implemented for dof_pos_target, dof_pos, dof_vel. --- gym/envs/mit_humanoid/mit_humanoid.py | 49 ++++++++++++++++++++ gym/envs/mit_humanoid/mit_humanoid_config.py | 19 ++------ 2 files changed, 52 insertions(+), 16 deletions(-) diff --git a/gym/envs/mit_humanoid/mit_humanoid.py b/gym/envs/mit_humanoid/mit_humanoid.py index 684f4cb3..a7fb56cc 100644 --- a/gym/envs/mit_humanoid/mit_humanoid.py +++ b/gym/envs/mit_humanoid/mit_humanoid.py @@ -15,6 +15,31 @@ def _init_buffers(self): self.oscillator_obs = torch.zeros(self.num_envs, 4, device=self.device) self.oscillator_freq = torch.zeros(self.num_envs, 2, device=self.device) + self.dof_pos_target_history = torch.zeros( + (self.num_envs, self.num_dof, 12), device=self.device + ) + self._init_history_buffers() + + def _init_history_buffers(self): + self.dof_pos_target_history = torch.zeros( + (self.num_envs, self.num_dof * self.cfg.env.history_length), + device=self.device, + ) + self.dof_pos_history = torch.zeros( + self.num_envs, + self.num_dof * self.cfg.env.history_length, + device=self.device, + ) + self.dof_vel_history = torch.zeros( + self.num_envs, + self.num_dof * self.cfg.env.history_length, + device=self.device, + ) + self.history_counter = torch.zeros(self.num_envs, dtype=int, device=self.device) + self.history_threshold = int( + self.cfg.control.ctrl_frequency / self.cfg.env.history_frequency + ) + def _reset_system(self, env_ids): if len(env_ids) == 0: return @@ -31,8 +56,15 @@ def _reset_system(self, env_ids): self.oscillator_obs = torch.cat( (self.oscillators.cos(), self.oscillators.sin()), dim=1 ) + self._reset_history_buffers(env_ids) return + def _reset_history_buffers(self, ids): + n = self.cfg.env.history_length + self.dof_pos_target_history[ids] = self.dof_pos_target[ids].tile(n) + self.dof_pos_history[ids] = self.dof_pos_target[ids].tile(n) + self.dof_vel_history[ids] = self.dof_pos_target[ids].tile(n) + # compute_torques accounting for coupling, and filtering torques def _compute_torques(self): torques = _apply_coupling( @@ -52,6 +84,7 @@ def _compute_torques(self): def _post_decimation_step(self): super()._post_decimation_step() self._step_oscillators() + self._update_history_buffers() def _step_oscillators(self, dt=None): if dt is None: @@ -62,6 +95,22 @@ def _step_oscillators(self, dt=None): (torch.cos(self.oscillators), torch.sin(self.oscillators)), dim=1 ) + def _update_history_buffers(self): + self.history_counter += 1 + + ids = torch.nonzero( + self.history_counter == self.history_threshold, as_tuple=False + ).flatten() + + self.dof_pos_target_history[ids].roll(self.num_dof, dims=1) # check + self.dof_pos_target_history[ids, : self.num_dof] = self.dof_pos_target[ids] + self.dof_pos_history[ids].roll(self.num_dof, dims=1) # check + self.dof_pos_history[ids, : self.num_dof] = self.dof_pos_target[ids] + self.dof_vel_history[ids].roll(self.num_dof, dims=1) # check + self.dof_vel_history[ids, : self.num_dof] = self.dof_pos_target[ids] + + self.history_counter[ids] = 0 + # --- rewards --- def _switch(self, mode=None): diff --git a/gym/envs/mit_humanoid/mit_humanoid_config.py b/gym/envs/mit_humanoid/mit_humanoid_config.py index 7a8680e8..04433381 100644 --- a/gym/envs/mit_humanoid/mit_humanoid_config.py +++ b/gym/envs/mit_humanoid/mit_humanoid_config.py @@ -12,7 +12,9 @@ class env(LeggedRobotCfg.env): num_observations = 49 + 3 * 18 # 121 num_actuators = 18 episode_length_s = 5 # episode length in seconds - num_privileged_obs = num_observations + + history_length = 3 # n samples + history_frequency = 10 # [Hz] class terrain(LeggedRobotCfg.terrain): pass @@ -172,21 +174,6 @@ class reward_settings(LeggedRobotCfg.reward_settings): switch_threshold = 0.2 class scaling(LeggedRobotCfg.scaling): - # * dimensionless time: sqrt(L/g) or sqrt(I/[mgL]), with I=I0+mL^2 - # virtual_leg_length = 0.65 - # dimensionless_time = (virtual_leg_length / 9.81) ** 0.5 - # base_height = virtual_leg_length - # base_lin_vel = virtual_leg_length / dimensionless_time - # base_ang_vel = 3.14 / dimensionless_time - # dof_vel = 20 # ought to be roughly max expected speed. - # height_measurements = virtual_leg_length - - # # todo check order of joints, create per-joint scaling - # dof_pos = 3.14 - # dof_pos_obs = dof_pos - # # * Action scales - # dof_pos_target = dof_pos - # tau_ff = 0.1 base_ang_vel = 2.5 base_lin_vel = 1.5 commands = 1 From 5020fd023f657116d0ebc5d254bcb98f20c2d215 Mon Sep 17 00:00:00 2001 From: Steve Heim Date: Mon, 18 Mar 2024 20:20:22 -0400 Subject: [PATCH 06/77] make pd gains specific for each robot in preparation for domain randomization of gains --- gym/envs/base/legged_robot.py | 33 ++++++-------------- gym/envs/mit_humanoid/mit_humanoid_config.py | 2 +- 2 files changed, 10 insertions(+), 25 deletions(-) diff --git a/gym/envs/base/legged_robot.py b/gym/envs/base/legged_robot.py index 1bc4623e..7fa2f0dd 100644 --- a/gym/envs/base/legged_robot.py +++ b/gym/envs/base/legged_robot.py @@ -481,41 +481,26 @@ def _init_buffers(self): get_axis_params(-1.0, self.up_axis_idx), device=self.device ).repeat((self.num_envs, 1)) self.torques = torch.zeros( - self.num_envs, - self.num_actuators, - dtype=torch.float, - device=self.device, + self.num_envs, self.num_actuators, dtype=torch.float, device=self.device ) self.p_gains = torch.zeros( - self.num_actuators, dtype=torch.float, device=self.device + self.num_envs, self.num_actuators, dtype=torch.float, device=self.device ) self.d_gains = torch.zeros( - self.num_actuators, dtype=torch.float, device=self.device + self.num_envs, self.num_actuators, dtype=torch.float, device=self.device ) self.dof_pos_target = torch.zeros( - self.num_envs, - self.num_actuators, - dtype=torch.float, - device=self.device, + self.num_envs, self.num_actuators, dtype=torch.float, device=self.device ) self.dof_vel_target = torch.zeros( - self.num_envs, - self.num_actuators, - dtype=torch.float, - device=self.device, + self.num_envs, self.num_actuators, dtype=torch.float, device=self.device ) self.tau_ff = torch.zeros( - self.num_envs, - self.num_actuators, - dtype=torch.float, - device=self.device, + self.num_envs, self.num_actuators, dtype=torch.float, device=self.device ) self.dof_pos_history = torch.zeros( - self.num_envs, - self.num_actuators * 3, - dtype=torch.float, - device=self.device, + self.num_envs, self.num_actuators * 3, dtype=torch.float, device=self.device ) self.commands = torch.zeros( self.num_envs, 3, dtype=torch.float, device=self.device @@ -582,8 +567,8 @@ def _init_buffers(self): found = False for dof_name in self.cfg.control.stiffness.keys(): if dof_name in name: - self.p_gains[i] = self.cfg.control.stiffness[dof_name] - self.d_gains[i] = self.cfg.control.damping[dof_name] + self.p_gains[:, i] = self.cfg.control.stiffness[dof_name] + self.d_gains[:, i] = self.cfg.control.damping[dof_name] found = True if not found: self.p_gains[i] = 0.0 diff --git a/gym/envs/mit_humanoid/mit_humanoid_config.py b/gym/envs/mit_humanoid/mit_humanoid_config.py index 04433381..1e079807 100644 --- a/gym/envs/mit_humanoid/mit_humanoid_config.py +++ b/gym/envs/mit_humanoid/mit_humanoid_config.py @@ -277,7 +277,7 @@ class weights: dof_near_home = 0.25 stance = 1.0 swing = 1.0 - walk_freq = 0.5 + walk_freq = 0.0 class termination_weight: termination = 15 From 82968b164fd94cbbfe95efb6d303877ed4ba6b2b Mon Sep 17 00:00:00 2001 From: Steve Heim Date: Tue, 19 Mar 2024 16:58:32 -0400 Subject: [PATCH 07/77] refactor sampled history to be more explicit and not overlap with `dof_pos_history` (used for smoothness reward). Some tuning. Change stance/swing reward to use the smoothed square wave. --- gym/envs/base/legged_robot.py | 12 +-- gym/envs/mit_humanoid/mit_humanoid.py | 95 ++++++++++++-------- gym/envs/mit_humanoid/mit_humanoid_config.py | 89 +++++++++--------- scripts/play.py | 2 +- 4 files changed, 109 insertions(+), 89 deletions(-) diff --git a/gym/envs/base/legged_robot.py b/gym/envs/base/legged_robot.py index 7fa2f0dd..d635fea7 100644 --- a/gym/envs/base/legged_robot.py +++ b/gym/envs/base/legged_robot.py @@ -110,10 +110,10 @@ def _post_decimation_step(self): self.base_height = self.root_states[:, 2:3] - n = self.num_actuators - self.dof_pos_history[:, 2 * n :] = self.dof_pos_history[:, n : 2 * n] - self.dof_pos_history[:, n : 2 * n] = self.dof_pos_history[:, :n] - self.dof_pos_history[:, :n] = self.dof_pos_target + self.dof_pos_history.roll(self.num_actuators) + self.dof_pos_history[:, : self.num_actuators] = ( + self.dof_pos - self.default_dof_pos + ) self.dof_pos_obs = self.dof_pos - self.default_dof_pos env_ids = ( @@ -133,7 +133,9 @@ def _reset_idx(self, env_ids): self._reset_system(env_ids) self._resample_commands(env_ids) # * reset buffers - self.dof_pos_history[env_ids] = 0.0 + self.dof_pos_history[env_ids] = ( + self.dof_pos[env_ids] - self.default_dof_pos + ).tile(3) self.episode_length_buf[env_ids] = 0 def _initialize_sim(self): diff --git a/gym/envs/mit_humanoid/mit_humanoid.py b/gym/envs/mit_humanoid/mit_humanoid.py index a7fb56cc..5db4ab7a 100644 --- a/gym/envs/mit_humanoid/mit_humanoid.py +++ b/gym/envs/mit_humanoid/mit_humanoid.py @@ -14,30 +14,28 @@ def _init_buffers(self): self.oscillators = torch.zeros(self.num_envs, 2, device=self.device) self.oscillator_obs = torch.zeros(self.num_envs, 4, device=self.device) self.oscillator_freq = torch.zeros(self.num_envs, 2, device=self.device) + self._init_sampled_history_buffers() - self.dof_pos_target_history = torch.zeros( - (self.num_envs, self.num_dof, 12), device=self.device - ) - self._init_history_buffers() - - def _init_history_buffers(self): - self.dof_pos_target_history = torch.zeros( - (self.num_envs, self.num_dof * self.cfg.env.history_length), + def _init_sampled_history_buffers(self): + self.sampled_history_dof_pos_target = torch.zeros( + (self.num_envs, self.num_dof * self.cfg.env.sampled_history_length), device=self.device, ) - self.dof_pos_history = torch.zeros( + self.sampled_history_dof_pos = torch.zeros( self.num_envs, - self.num_dof * self.cfg.env.history_length, + self.num_dof * self.cfg.env.sampled_history_length, device=self.device, ) - self.dof_vel_history = torch.zeros( + self.sampled_history_dof_vel = torch.zeros( self.num_envs, - self.num_dof * self.cfg.env.history_length, + self.num_dof * self.cfg.env.sampled_history_length, device=self.device, ) - self.history_counter = torch.zeros(self.num_envs, dtype=int, device=self.device) - self.history_threshold = int( - self.cfg.control.ctrl_frequency / self.cfg.env.history_frequency + self.sampled_history_counter = torch.zeros( + self.num_envs, dtype=int, device=self.device + ) + self.sampled_history_threshold = int( + self.cfg.control.ctrl_frequency / self.cfg.env.sampled_history_frequency ) def _reset_system(self, env_ids): @@ -56,14 +54,14 @@ def _reset_system(self, env_ids): self.oscillator_obs = torch.cat( (self.oscillators.cos(), self.oscillators.sin()), dim=1 ) - self._reset_history_buffers(env_ids) + self._reset_sampled_history_buffers(env_ids) return - def _reset_history_buffers(self, ids): - n = self.cfg.env.history_length - self.dof_pos_target_history[ids] = self.dof_pos_target[ids].tile(n) - self.dof_pos_history[ids] = self.dof_pos_target[ids].tile(n) - self.dof_vel_history[ids] = self.dof_pos_target[ids].tile(n) + def _reset_sampled_history_buffers(self, ids): + n = self.cfg.env.sampled_history_length + self.sampled_history_dof_pos_target[ids] = self.dof_pos_target[ids].tile(n) + self.sampled_history_dof_pos[ids] = self.dof_pos_target[ids].tile(n) + self.sampled_history_dof_vel[ids] = self.dof_pos_target[ids].tile(n) # compute_torques accounting for coupling, and filtering torques def _compute_torques(self): @@ -84,7 +82,7 @@ def _compute_torques(self): def _post_decimation_step(self): super()._post_decimation_step() self._step_oscillators() - self._update_history_buffers() + self._update_sampled_history_buffers() def _step_oscillators(self, dt=None): if dt is None: @@ -95,21 +93,24 @@ def _step_oscillators(self, dt=None): (torch.cos(self.oscillators), torch.sin(self.oscillators)), dim=1 ) - def _update_history_buffers(self): - self.history_counter += 1 + def _update_sampled_history_buffers(self): + self.sampled_history_counter += 1 ids = torch.nonzero( - self.history_counter == self.history_threshold, as_tuple=False + self.sampled_history_counter == self.sampled_history_threshold, + as_tuple=False, ).flatten() - self.dof_pos_target_history[ids].roll(self.num_dof, dims=1) # check - self.dof_pos_target_history[ids, : self.num_dof] = self.dof_pos_target[ids] - self.dof_pos_history[ids].roll(self.num_dof, dims=1) # check - self.dof_pos_history[ids, : self.num_dof] = self.dof_pos_target[ids] - self.dof_vel_history[ids].roll(self.num_dof, dims=1) # check - self.dof_vel_history[ids, : self.num_dof] = self.dof_pos_target[ids] + self.sampled_history_dof_pos_target[ids].roll(self.num_dof, dims=1) # check + self.sampled_history_dof_pos_target[ids, : self.num_dof] = self.dof_pos_target[ + ids + ] + self.sampled_history_dof_pos[ids].roll(self.num_dof, dims=1) # check + self.sampled_history_dof_pos[ids, : self.num_dof] = self.dof_pos_target[ids] + self.sampled_history_dof_vel[ids].roll(self.num_dof, dims=1) # check + self.sampled_history_dof_vel[ids, : self.num_dof] = self.dof_pos_target[ids] - self.history_counter[ids] = 0 + self.sampled_history_counter[ids] = 0 # --- rewards --- @@ -167,7 +168,7 @@ def _reward_dof_vel(self): def _reward_dof_near_home(self): return torch.mean( self._sqrdexp( - (self.dof_pos - self.default_dof_pos) / self.scales["dof_pos_obs"] + (self.dof_pos - self.default_dof_pos) / self.scales["dof_pos"] ), dim=1, ) @@ -184,15 +185,17 @@ def _reward_stand_still(self): return rew_vel + rew_pos - rew_base_vel * self._switch("stand") def _reward_stance(self): - phase = torch.maximum( - torch.zeros_like(self.oscillators), -self.oscillators.sin() - ) # positive during swing, negative during stance + # phase = torch.maximum( + # torch.zeros_like(self.oscillators), -self.oscillators.sin() + # ) # positive during swing, negative during stance + phase = self.smooth_sqr_wave(self.oscillators) return (phase * self._compute_grf()).mean(dim=1) def _reward_swing(self): - phase = torch.maximum( - torch.zeros_like(self.oscillators), self.oscillators.sin() - ) # positive during swing, negative during stance + # phase = torch.maximum( + # torch.zeros_like(self.oscillators), self.oscillators.sin() + # ) # positive during swing, negative during stance + phase = self.smooth_sqr_wave(self.oscillators + torch.pi) return -(phase * self._compute_grf()).mean(dim=1) def _compute_grf(self, grf_norm=True): @@ -202,6 +205,9 @@ def _compute_grf(self, grf_norm=True): else: return grf + def smooth_sqr_wave(self, phase, sigma=0.2): # sigma=0 is step function + return phase.sin() / (2 * torch.sqrt(phase.sin() ** 2.0 + sigma**2.0)) + 0.5 + def _reward_walk_freq(self): # Penalize deviation from base frequency return torch.mean( @@ -211,3 +217,14 @@ def _reward_walk_freq(self): ), dim=1, ) * self._switch("move") + + def _reward_hips_forward(self): + # reward hip motors for pointing forward + hip_yaw_abad = torch.stack((self.dof_pos[:, 0:2], self.dof_pos[:, 5:7]), dim=1) + hip_yaw_abad -= torch.stack( + (self.default_dof_pos[:, 0:2], self.default_dof_pos[:, 5:7]), dim=1 + ) + hip_yaw_abad /= torch.stack( + (self.scales["dof_pos"][0:2], self.scales["dof_pos"][5:7]), dim=1 + ) + return self._sqrdexp(hip_yaw_abad).sum(dim=1).mean(dim=1) diff --git a/gym/envs/mit_humanoid/mit_humanoid_config.py b/gym/envs/mit_humanoid/mit_humanoid_config.py index 1e079807..89359a8c 100644 --- a/gym/envs/mit_humanoid/mit_humanoid_config.py +++ b/gym/envs/mit_humanoid/mit_humanoid_config.py @@ -9,12 +9,11 @@ class MITHumanoidCfg(LeggedRobotCfg): class env(LeggedRobotCfg.env): num_envs = 4096 - num_observations = 49 + 3 * 18 # 121 num_actuators = 18 episode_length_s = 5 # episode length in seconds - history_length = 3 # n samples - history_frequency = 10 # [Hz] + sampled_history_length = 3 # n samples + sampled_history_frequency = 10 # [Hz] class terrain(LeggedRobotCfg.terrain): pass @@ -29,17 +28,17 @@ class init_state(LeggedRobotCfg.init_state): default_joint_angles = { "hip_yaw": 0.0, "hip_abad": 0.0, - "hip_pitch": -0.4, - "knee": 0.9, - "ankle": -0.45, + "hip_pitch": -0.667751, + "knee": 1.4087, + "ankle": -0.708876, "shoulder_pitch": 0.0, "shoulder_abad": 0.0, "shoulder_yaw": 0.0, - "elbow": 0.0, + "elbow": -1.25, } # * default COM for basic initialization - pos = [0.0, 0.0, 0.66] # x,y,z [m] + pos = [0.0, 0.0, 0.72] # x,y,z [m] rot = [0.0, 0.0, 0.0, 1.0] # x,y,z,w [quat] lin_vel = [0.0, 0.0, 0.0] # x,y,z [m/s] ang_vel = [0.0, 0.0, 0.0] # x,y,z [rad/s] @@ -47,22 +46,22 @@ class init_state(LeggedRobotCfg.init_state): # * initialization for random range setup dof_pos_range = { - "hip_yaw": [0.0, 0.0], - "hip_abad": [0.0, 0.0], - "hip_pitch": [-0.29, -0.25], - "knee": [0.67, 0.71], - "ankle": [-0.43, -0.39], + "hip_yaw": [-0.1, 0.1], + "hip_abad": [-0.1, 0.1], + "hip_pitch": [-0.2, 0.2], + "knee": [-0.2, 0.2], + "ankle": [-0.15, 0.15], "shoulder_pitch": [0.0, 0.0], "shoulder_abad": [0.0, 0.0], "shoulder_yaw": [0.0, 0.0], - "elbow": [0.0, 0.0], + "elbow": [0, 0], } dof_vel_range = { - "hip_yaw": [-0.0, 0.1], - "hip_abad": [-0.0, 0.1], - "hip_pitch": [-0.1, -0.1], - "knee": [-0.05, 0.05], - "ankle": [-0.05, 0.05], + "hip_yaw": [-0.1, 0.1], + "hip_abad": [-0.1, 0.1], + "hip_pitch": [-0.1, 0.1], + "knee": [-0.1, 0.1], + "ankle": [-0.1, 0.1], "shoulder_pitch": [0.0, 0.0], "shoulder_abad": [0.0, 0.0], "shoulder_yaw": [0.0, 0.0], @@ -72,15 +71,15 @@ class init_state(LeggedRobotCfg.init_state): root_pos_range = [ [0.0, 0.0], # x [0.0, 0.0], # y - [0.7, 0.8], # z + [0.72, 0.72], # z [-0.1, 0.1], # roll [-0.1, 0.1], # pitch [-0.1, 0.1], ] # yaw root_vel_range = [ - [-0.5, 2.5], # x - [-0.5, 0.5], # y + [-0.75, 2.75], # x + [-0.55, 0.55], # y [-0.35, 0.1], # z [-0.35, 0.35], # roll [-0.35, 0.35], # pitch @@ -98,7 +97,7 @@ class control(LeggedRobotCfg.control): "shoulder_pitch": 40.0, "shoulder_abad": 40.0, "shoulder_yaw": 40.0, - "elbow": 40.0, + "elbow": 50.0, } # [N*m/rad] damping = { "hip_yaw": 2.0, @@ -118,19 +117,19 @@ class control(LeggedRobotCfg.control): filter_gain = 0.1586 # 1: no filtering, 0: wall class oscillator: - base_frequency = 1.5 # [Hz] + base_frequency = 2.0 # [Hz] class commands: resampling_time = 10.0 # time before command are changed[s] class ranges: - lin_vel_x = [-1.0, 4.0] # min max [m/s] - lin_vel_y = 1.0 # max [m/s] - yaw_vel = 1 # max [rad/s] + lin_vel_x = [-1.0, 4.0] # min max [m/s] [-0.75, 0.75] + lin_vel_y = 0.35 # max [m/s] + yaw_vel = 0.5 # max [rad/s] class push_robots: - toggle = False - interval_s = 2 + toggle = True + interval_s = 1 max_push_vel_xy = 0.6 push_box_dims = [0.1, 0.1, 0.3] # x,y,z [m] @@ -147,11 +146,11 @@ class asset(LeggedRobotCfg.asset): ) # foot_collisionbox_names = ["foot"] foot_name = "foot" - penalize_contacts_on = ["base", "arm"] + penalize_contacts_on = ["arm"] terminate_after_contacts_on = ["base"] - end_effector_names = ["hand", "foot"] + end_effector_names = ["hand", "foot"] # ?? flip_visual_attachments = False - self_collisions = 1 # 1 to disagble, 0 to enable...bitwise filter + self_collisions = 0 # 1 to disagble, 0 to enable...bitwise filter collapse_fixed_joints = False # * see GymDofDriveModeFlags # * (0 is none, 1 is pos tgt, 2 is vel tgt, 3 effort) @@ -198,7 +197,6 @@ class scaling(LeggedRobotCfg.scaling): 0.1, 0.1, ] - dof_pos_obs = dof_pos # # * Action scales dof_pos_target = dof_pos dof_vel = [ @@ -221,7 +219,7 @@ class scaling(LeggedRobotCfg.scaling): 1.0, 1.0, ] - dof_pos_history = 3 * dof_pos_obs + dof_pos_history = 3 * dof_pos class MITHumanoidRunnerCfg(LeggedRobotRunnerCfg): @@ -231,8 +229,8 @@ class MITHumanoidRunnerCfg(LeggedRobotRunnerCfg): class policy(LeggedRobotRunnerCfg.policy): disable_actions = False init_noise_std = 1.0 - actor_hidden_dims = [512, 256, 128] - critic_hidden_dims = [512, 256, 128] + actor_hidden_dims = [256, 256, 128] + critic_hidden_dims = [256, 256, 128] # * can be elu, relu, selu, crelu, lrelu, tanh, sigmoid activation = "tanh" @@ -245,6 +243,9 @@ class policy(LeggedRobotRunnerCfg.policy): "dof_pos_obs", "dof_vel", "dof_pos_history", + "sampled_history_dof_pos", + "sampled_history_dof_vel", + "sampled_history_dof_pos_target", "oscillator_obs", ] critic_obs = actor_obs @@ -252,18 +253,17 @@ class policy(LeggedRobotRunnerCfg.policy): actions = ["dof_pos_target"] class noise: - base_height = 0.05 - dof_pos_obs = 0.0 - dof_vel = 0.0 - base_lin_vel = 0.1 - base_ang_vel = 0.2 - projected_gravity = 0.05 - height_measurements = 0.1 + dof_pos = 0.005 + dof_vel = 0.05 + base_ang_vel = 0.025 + base_lin_vel = 0.025 + projected_gravity = 0.01 + feet_contact_state = 0.025 class reward: class weights: - tracking_ang_vel = 1.5 tracking_lin_vel = 3.0 + tracking_ang_vel = 1.5 orientation = 1.0 torques = 5.0e-4 min_base_height = 1.5 @@ -277,6 +277,7 @@ class weights: dof_near_home = 0.25 stance = 1.0 swing = 1.0 + hips_forward = 0.5 walk_freq = 0.0 class termination_weight: diff --git a/scripts/play.py b/scripts/play.py index 29a837ea..eb32855e 100644 --- a/scripts/play.py +++ b/scripts/play.py @@ -18,7 +18,7 @@ def setup(args): env_cfg.env.num_projectiles = 20 task_registry.make_gym_and_sim() env = task_registry.make_env(args.task, env_cfg) - env.cfg.init_state.reset_mode = "reset_to_basic" + # env.cfg.init_state.reset_mode = "reset_to_basic" train_cfg.runner.resume = True train_cfg.logging.enable_local_saving = False runner = task_registry.make_alg_runner(env, train_cfg) From 3ae04a080730dd24269d36c222ecc091e936d311 Mon Sep 17 00:00:00 2001 From: Steve Heim Date: Sat, 23 Mar 2024 12:29:28 -0400 Subject: [PATCH 08/77] properly roll histories, retune action_rate for mc --- gym/envs/base/legged_robot.py | 2 +- .../mini_cheetah/mini_cheetah_ref_config.py | 4 +-- gym/envs/mit_humanoid/mit_humanoid.py | 12 +++++-- gym/envs/mit_humanoid/mit_humanoid_config.py | 36 +++++++++---------- 4 files changed, 30 insertions(+), 24 deletions(-) diff --git a/gym/envs/base/legged_robot.py b/gym/envs/base/legged_robot.py index d635fea7..68178650 100644 --- a/gym/envs/base/legged_robot.py +++ b/gym/envs/base/legged_robot.py @@ -110,7 +110,7 @@ def _post_decimation_step(self): self.base_height = self.root_states[:, 2:3] - self.dof_pos_history.roll(self.num_actuators) + self.dof_pos_history = self.dof_pos_history.roll(self.num_actuators) self.dof_pos_history[:, : self.num_actuators] = ( self.dof_pos - self.default_dof_pos ) diff --git a/gym/envs/mini_cheetah/mini_cheetah_ref_config.py b/gym/envs/mini_cheetah/mini_cheetah_ref_config.py index 7911206a..1a4e0aed 100644 --- a/gym/envs/mini_cheetah/mini_cheetah_ref_config.py +++ b/gym/envs/mini_cheetah/mini_cheetah_ref_config.py @@ -113,8 +113,8 @@ class weights: dof_vel = 0.0 min_base_height = 1.5 collision = 0.0 - action_rate = 0.01 - action_rate2 = 0.001 + action_rate = 1.0 + action_rate2 = 1.0 stand_still = 0.0 dof_pos_limits = 0.0 feet_contact_forces = 0.0 diff --git a/gym/envs/mit_humanoid/mit_humanoid.py b/gym/envs/mit_humanoid/mit_humanoid.py index 5db4ab7a..489b463d 100644 --- a/gym/envs/mit_humanoid/mit_humanoid.py +++ b/gym/envs/mit_humanoid/mit_humanoid.py @@ -101,13 +101,19 @@ def _update_sampled_history_buffers(self): as_tuple=False, ).flatten() - self.sampled_history_dof_pos_target[ids].roll(self.num_dof, dims=1) # check + self.sampled_history_dof_pos_target[ids] = torch.roll( + self.sampled_history_dof_pos_target[ids], self.num_dof, dims=1 + ) # check self.sampled_history_dof_pos_target[ids, : self.num_dof] = self.dof_pos_target[ ids ] - self.sampled_history_dof_pos[ids].roll(self.num_dof, dims=1) # check + self.sampled_history_dof_pos[ids] = torch.roll( + self.sampled_history_dof_pos[ids], self.num_dof, dims=1 + ) # check self.sampled_history_dof_pos[ids, : self.num_dof] = self.dof_pos_target[ids] - self.sampled_history_dof_vel[ids].roll(self.num_dof, dims=1) # check + self.sampled_history_dof_vel[ids] = torch.roll( + self.sampled_history_dof_vel[ids], self.num_dof, dims=1 + ) # check self.sampled_history_dof_vel[ids, : self.num_dof] = self.dof_pos_target[ids] self.sampled_history_counter[ids] = 0 diff --git a/gym/envs/mit_humanoid/mit_humanoid_config.py b/gym/envs/mit_humanoid/mit_humanoid_config.py index 89359a8c..54813f7b 100644 --- a/gym/envs/mit_humanoid/mit_humanoid_config.py +++ b/gym/envs/mit_humanoid/mit_humanoid_config.py @@ -38,7 +38,7 @@ class init_state(LeggedRobotCfg.init_state): } # * default COM for basic initialization - pos = [0.0, 0.0, 0.72] # x,y,z [m] + pos = [0.0, 0.0, 0.6] # x,y,z [m] rot = [0.0, 0.0, 0.0, 1.0] # x,y,z,w [quat] lin_vel = [0.0, 0.0, 0.0] # x,y,z [m/s] ang_vel = [0.0, 0.0, 0.0] # x,y,z [rad/s] @@ -46,11 +46,11 @@ class init_state(LeggedRobotCfg.init_state): # * initialization for random range setup dof_pos_range = { - "hip_yaw": [-0.1, 0.1], - "hip_abad": [-0.1, 0.1], - "hip_pitch": [-0.2, 0.2], - "knee": [-0.2, 0.2], - "ankle": [-0.15, 0.15], + "hip_yaw": [-0.0, 0.0], + "hip_abad": [-0.0, 0.0], + "hip_pitch": [-0.667751, -0.667751], + "knee": [1.4087, 1.4087], + "ankle": [-0.708876, -0.708876], "shoulder_pitch": [0.0, 0.0], "shoulder_abad": [0.0, 0.0], "shoulder_yaw": [0.0, 0.0], @@ -71,7 +71,7 @@ class init_state(LeggedRobotCfg.init_state): root_pos_range = [ [0.0, 0.0], # x [0.0, 0.0], # y - [0.72, 0.72], # z + [0.64, 0.7], # z [-0.1, 0.1], # roll [-0.1, 0.1], # pitch [-0.1, 0.1], @@ -250,7 +250,7 @@ class policy(LeggedRobotRunnerCfg.policy): ] critic_obs = actor_obs - actions = ["dof_pos_target"] + actions = ["dof_pos_target", "oscillator_freq"] class noise: dof_pos = 0.005 @@ -262,23 +262,23 @@ class noise: class reward: class weights: - tracking_lin_vel = 3.0 - tracking_ang_vel = 1.5 - orientation = 1.0 + # tracking_lin_vel = 3.0 + # tracking_ang_vel = 1.5 + # orientation = 1.0 torques = 5.0e-4 - min_base_height = 1.5 + # min_base_height = 1.5 action_rate = 0.01 action_rate2 = 0.001 lin_vel_z = 0.0 ang_vel_xy = 0.0 - dof_vel = 0.25 - stand_still = 0.25 + # dof_vel = 0.25 + # stand_still = 0.25 dof_pos_limits = 0.25 - dof_near_home = 0.25 + # dof_near_home = 0.25 stance = 1.0 swing = 1.0 hips_forward = 0.5 - walk_freq = 0.0 + walk_freq = 2.5 class termination_weight: termination = 15 @@ -288,11 +288,11 @@ class algorithm(LeggedRobotRunnerCfg.algorithm): value_loss_coef = 1.0 use_clipped_value_loss = True clip_param = 0.2 - entropy_coef = 0.002 + entropy_coef = 0.01 num_learning_epochs = 5 # * mini batch size = num_envs*nsteps / nminibatches num_mini_batches = 4 - learning_rate = 5.0e-4 + learning_rate = 5.0e-5 schedule = "adaptive" # could be adaptive, fixed gamma = 0.999 lam = 0.95 From ee05de48a620bcf598002a4b76779d9d92be3612 Mon Sep 17 00:00:00 2001 From: Steve Heim Date: Sun, 24 Mar 2024 18:30:55 -0400 Subject: [PATCH 09/77] enable arms in learnt urdf diff --git a/gym/envs/mit_humanoid/mit_humanoid_config.py b/gym/envs/mit_humanoid/mit_humanoid_config.py index 54813f7..368f2c3 100644 --- a/gym/envs/mit_humanoid/mit_humanoid_config.py +++ b/gym/envs/mit_humanoid/mit_humanoid_config.py @@ -142,7 +142,7 @@ class MITHumanoidCfg(LeggedRobotCfg): class asset(LeggedRobotCfg.asset): file = ( "{LEGGED_GYM_ROOT_DIR}/resources/robots/" - + "mit_humanoid/urdf/humanoid_R_sf.urdf" + + "mit_humanoid/urdf/humanoid_F_sf_learnt.urdf" ) # foot_collisionbox_names = ["foot"] foot_name = "foot" @@ -289,12 +289,12 @@ class MITHumanoidRunnerCfg(LeggedRobotRunnerCfg): use_clipped_value_loss = True clip_param = 0.2 entropy_coef = 0.01 - num_learning_epochs = 5 + num_learning_epochs = 4 # * mini batch size = num_envs*nsteps / nminibatches num_mini_batches = 4 - learning_rate = 5.0e-5 + learning_rate = 1.0e-6 schedule = "adaptive" # could be adaptive, fixed - gamma = 0.999 + gamma = 0.99 lam = 0.95 desired_kl = 0.01 max_grad_norm = 1.0 diff --git a/resources/robots/mit_humanoid/urdf/humanoid_F_sf_learnt.urdf b/resources/robots/mit_humanoid/urdf/humanoid_F_sf_learnt.urdf index 5e76f34..98a0773 100644 --- a/resources/robots/mit_humanoid/urdf/humanoid_F_sf_learnt.urdf +++ b/resources/robots/mit_humanoid/urdf/humanoid_F_sf_learnt.urdf @@ -570,7 +570,7 @@ Simple Foot: foot approximated as single box-contact --> + type="revolute"> @@ -615,7 +615,7 @@ Simple Foot: foot approximated as single box-contact --> + type="revolute"> @@ -668,7 +668,7 @@ Simple Foot: foot approximated as single box-contact --> + type="revolute"> @@ -719,7 +719,7 @@ Simple Foot: foot approximated as single box-contact --> + type="revolute"> @@ -798,7 +798,7 @@ Simple Foot: foot approximated as single box-contact --> + type="revolute"> @@ -843,7 +843,7 @@ Simple Foot: foot approximated as single box-contact --> + type="revolute"> @@ -896,7 +896,7 @@ Simple Foot: foot approximated as single box-contact --> + type="revolute"> @@ -947,7 +947,7 @@ Simple Foot: foot approximated as single box-contact --> + type="revolute"> --- gym/envs/mit_humanoid/mit_humanoid_config.py | 8 ++++---- .../mit_humanoid/urdf/humanoid_F_sf_learnt.urdf | 16 ++++++++-------- 2 files changed, 12 insertions(+), 12 deletions(-) diff --git a/gym/envs/mit_humanoid/mit_humanoid_config.py b/gym/envs/mit_humanoid/mit_humanoid_config.py index 54813f7b..368f2c3b 100644 --- a/gym/envs/mit_humanoid/mit_humanoid_config.py +++ b/gym/envs/mit_humanoid/mit_humanoid_config.py @@ -142,7 +142,7 @@ class domain_rand: class asset(LeggedRobotCfg.asset): file = ( "{LEGGED_GYM_ROOT_DIR}/resources/robots/" - + "mit_humanoid/urdf/humanoid_R_sf.urdf" + + "mit_humanoid/urdf/humanoid_F_sf_learnt.urdf" ) # foot_collisionbox_names = ["foot"] foot_name = "foot" @@ -289,12 +289,12 @@ class algorithm(LeggedRobotRunnerCfg.algorithm): use_clipped_value_loss = True clip_param = 0.2 entropy_coef = 0.01 - num_learning_epochs = 5 + num_learning_epochs = 4 # * mini batch size = num_envs*nsteps / nminibatches num_mini_batches = 4 - learning_rate = 5.0e-5 + learning_rate = 1.0e-6 schedule = "adaptive" # could be adaptive, fixed - gamma = 0.999 + gamma = 0.99 lam = 0.95 desired_kl = 0.01 max_grad_norm = 1.0 diff --git a/resources/robots/mit_humanoid/urdf/humanoid_F_sf_learnt.urdf b/resources/robots/mit_humanoid/urdf/humanoid_F_sf_learnt.urdf index 5e76f34a..98a0773a 100644 --- a/resources/robots/mit_humanoid/urdf/humanoid_F_sf_learnt.urdf +++ b/resources/robots/mit_humanoid/urdf/humanoid_F_sf_learnt.urdf @@ -570,7 +570,7 @@ Simple Foot: foot approximated as single box-contact --> + type="revolute"> @@ -615,7 +615,7 @@ Simple Foot: foot approximated as single box-contact --> + type="revolute"> @@ -668,7 +668,7 @@ Simple Foot: foot approximated as single box-contact --> + type="revolute"> @@ -719,7 +719,7 @@ Simple Foot: foot approximated as single box-contact --> + type="revolute"> @@ -798,7 +798,7 @@ Simple Foot: foot approximated as single box-contact --> + type="revolute"> @@ -843,7 +843,7 @@ Simple Foot: foot approximated as single box-contact --> + type="revolute"> @@ -896,7 +896,7 @@ Simple Foot: foot approximated as single box-contact --> + type="revolute"> @@ -947,7 +947,7 @@ Simple Foot: foot approximated as single box-contact --> + type="revolute"> From 0aab1005ea71ad3b39712ad4a5dcbd81845d3c8c Mon Sep 17 00:00:00 2001 From: Steve Heim Date: Sun, 24 Mar 2024 19:15:55 -0400 Subject: [PATCH 10/77] hotfix old runner, and set as default for humanoid. New runner is numerically unstable for humanoid --- gym/envs/mit_humanoid/mit_humanoid_config.py | 4 ++-- learning/runners/old_policy_runner.py | 2 +- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/gym/envs/mit_humanoid/mit_humanoid_config.py b/gym/envs/mit_humanoid/mit_humanoid_config.py index 368f2c3b..a3e113b6 100644 --- a/gym/envs/mit_humanoid/mit_humanoid_config.py +++ b/gym/envs/mit_humanoid/mit_humanoid_config.py @@ -224,7 +224,7 @@ class scaling(LeggedRobotCfg.scaling): class MITHumanoidRunnerCfg(LeggedRobotRunnerCfg): seed = -1 - runner_class_name = "OnPolicyRunner" + runner_class_name = "OldPolicyRunner" class policy(LeggedRobotRunnerCfg.policy): disable_actions = False @@ -301,7 +301,7 @@ class algorithm(LeggedRobotRunnerCfg.algorithm): class runner(LeggedRobotRunnerCfg.runner): policy_class_name = "ActorCritic" - algorithm_class_name = "PPO2" + algorithm_class_name = "PPO" num_steps_per_env = 32 max_iterations = 1000 run_name = "Standing" diff --git a/learning/runners/old_policy_runner.py b/learning/runners/old_policy_runner.py index 9a738b93..f42b26cf 100644 --- a/learning/runners/old_policy_runner.py +++ b/learning/runners/old_policy_runner.py @@ -70,7 +70,7 @@ def learn(self): logger.tic("learning") self.alg.update() logger.toc("learning") - logger.log_category() + logger.log_all_categories() logger.finish_iteration() logger.toc("iteration") From d86162e57f3d2c64ed28f1a4a172bc3b7a90efbd Mon Sep 17 00:00:00 2001 From: Steve Heim Date: Fri, 29 Mar 2024 13:04:45 -0400 Subject: [PATCH 11/77] WIP: tuning, critic loss higher than expected --- gym/envs/mit_humanoid/mit_humanoid.py | 12 ++--- gym/envs/mit_humanoid/mit_humanoid_config.py | 48 ++++++++++---------- learning/algorithms/ppo2.py | 13 ++---- learning/runners/on_policy_runner.py | 4 ++ learning/utils/dict_utils.py | 23 ++++------ 5 files changed, 48 insertions(+), 52 deletions(-) diff --git a/gym/envs/mit_humanoid/mit_humanoid.py b/gym/envs/mit_humanoid/mit_humanoid.py index 489b463d..118ac170 100644 --- a/gym/envs/mit_humanoid/mit_humanoid.py +++ b/gym/envs/mit_humanoid/mit_humanoid.py @@ -172,12 +172,9 @@ def _reward_dof_vel(self): ) * self._switch("stand") def _reward_dof_near_home(self): - return torch.mean( - self._sqrdexp( - (self.dof_pos - self.default_dof_pos) / self.scales["dof_pos"] - ), - dim=1, - ) + return self._sqrdexp( + (self.dof_pos - self.default_dof_pos) / self.scales["dof_pos"] + ).mean(dim=1) def _reward_stand_still(self): """Penalize motion at zero commands""" @@ -233,4 +230,5 @@ def _reward_hips_forward(self): hip_yaw_abad /= torch.stack( (self.scales["dof_pos"][0:2], self.scales["dof_pos"][5:7]), dim=1 ) - return self._sqrdexp(hip_yaw_abad).sum(dim=1).mean(dim=1) + return (hip_yaw_abad).pow(2).mean(dim=1) + # return self._sqrdexp(hip_yaw_abad).sum(dim=1).mean(dim=1) diff --git a/gym/envs/mit_humanoid/mit_humanoid_config.py b/gym/envs/mit_humanoid/mit_humanoid_config.py index a3e113b6..033409e0 100644 --- a/gym/envs/mit_humanoid/mit_humanoid_config.py +++ b/gym/envs/mit_humanoid/mit_humanoid_config.py @@ -117,20 +117,20 @@ class control(LeggedRobotCfg.control): filter_gain = 0.1586 # 1: no filtering, 0: wall class oscillator: - base_frequency = 2.0 # [Hz] + base_frequency = 3.0 # [Hz] class commands: resampling_time = 10.0 # time before command are changed[s] class ranges: - lin_vel_x = [-1.0, 4.0] # min max [m/s] [-0.75, 0.75] - lin_vel_y = 0.35 # max [m/s] - yaw_vel = 0.5 # max [rad/s] + lin_vel_x = [-0.0, 0.0] # min max [m/s] [-0.75, 0.75] + lin_vel_y = 0.0 # max [m/s] + yaw_vel = 0.0 # max [rad/s] class push_robots: toggle = True interval_s = 1 - max_push_vel_xy = 0.6 + max_push_vel_xy = 0.5 push_box_dims = [0.1, 0.1, 0.3] # x,y,z [m] class domain_rand: @@ -155,18 +155,18 @@ class asset(LeggedRobotCfg.asset): # * see GymDofDriveModeFlags # * (0 is none, 1 is pos tgt, 2 is vel tgt, 3 effort) default_dof_drive_mode = 3 - fix_base_link = False + fix_base_link = True disable_gravity = False disable_motors = False total_mass = 25.0 class reward_settings(LeggedRobotCfg.reward_settings): - soft_dof_pos_limit = 0.9 - soft_dof_vel_limit = 0.9 - soft_torque_limit = 0.9 + soft_dof_pos_limit = 0.8 + soft_dof_vel_limit = 0.8 + soft_torque_limit = 0.8 max_contact_force = 1500.0 base_height_target = BASE_HEIGHT_REF - tracking_sigma = 0.5 + tracking_sigma = 0.25 # a smooth switch based on |cmd| (commanded velocity). switch_scale = 0.5 @@ -229,28 +229,28 @@ class MITHumanoidRunnerCfg(LeggedRobotRunnerCfg): class policy(LeggedRobotRunnerCfg.policy): disable_actions = False init_noise_std = 1.0 - actor_hidden_dims = [256, 256, 128] - critic_hidden_dims = [256, 256, 128] + actor_hidden_dims = [512, 512, 128] + critic_hidden_dims = [512, 256, 128] # * can be elu, relu, selu, crelu, lrelu, tanh, sigmoid - activation = "tanh" + activation = "elu" actor_obs = [ "base_height", "base_lin_vel", "base_ang_vel", "projected_gravity", - "commands", + # "commands", "dof_pos_obs", "dof_vel", "dof_pos_history", - "sampled_history_dof_pos", - "sampled_history_dof_vel", - "sampled_history_dof_pos_target", + # "sampled_history_dof_pos", + # "sampled_history_dof_vel", + # "sampled_history_dof_pos_target", "oscillator_obs", ] critic_obs = actor_obs - actions = ["dof_pos_target", "oscillator_freq"] + actions = ["dof_pos_target"] class noise: dof_pos = 0.005 @@ -262,23 +262,23 @@ class noise: class reward: class weights: - # tracking_lin_vel = 3.0 + # tracking_lin_vel = 0.0 # tracking_ang_vel = 1.5 # orientation = 1.0 torques = 5.0e-4 # min_base_height = 1.5 - action_rate = 0.01 - action_rate2 = 0.001 + action_rate = 1e-3 + action_rate2 = 1e-3 lin_vel_z = 0.0 ang_vel_xy = 0.0 # dof_vel = 0.25 # stand_still = 0.25 dof_pos_limits = 0.25 - # dof_near_home = 0.25 + dof_near_home = 0.25 stance = 1.0 swing = 1.0 - hips_forward = 0.5 - walk_freq = 2.5 + hips_forward = 0.0 + walk_freq = 0.0 # 2.5 class termination_weight: termination = 15 diff --git a/learning/algorithms/ppo2.py b/learning/algorithms/ppo2.py index 69d6dcd2..efdce668 100644 --- a/learning/algorithms/ppo2.py +++ b/learning/algorithms/ppo2.py @@ -65,15 +65,12 @@ def train_mode(self): def act(self, obs, critic_obs): return self.actor.act(obs).detach() - def update(self, data, last_obs=None): - if last_obs is None: - last_values = None - else: - with torch.no_grad(): - last_values = self.critic.evaluate(last_obs).detach() - compute_generalized_advantages( - data, self.gamma, self.lam, self.critic, last_values + def update(self, data): + data["values"] = self.critic.evaluate(data["critic_obs"]) + data["advantages"] = compute_generalized_advantages( + data, self.gamma, self.lam, self.critic ) + data["returns"] = data["advantages"] + data["values"] self.update_critic(data) self.update_actor(data) diff --git a/learning/runners/on_policy_runner.py b/learning/runners/on_policy_runner.py index 91e2ac26..6350eaa5 100644 --- a/learning/runners/on_policy_runner.py +++ b/learning/runners/on_policy_runner.py @@ -38,8 +38,10 @@ def learn(self): transition.update( { "actor_obs": actor_obs, + "next_actor_obs": actor_obs, "actions": self.alg.act(actor_obs, critic_obs), "critic_obs": critic_obs, + "next_critic_obs": critic_obs, "rewards": self.get_rewards({"termination": 0.0})["termination"], "dones": self.get_timed_out(), } @@ -89,6 +91,8 @@ def learn(self): transition.update( { + "next_actor_obs": actor_obs, + "next_critic_obs": critic_obs, "rewards": total_rewards, "timed_out": timed_out, "dones": dones, diff --git a/learning/utils/dict_utils.py b/learning/utils/dict_utils.py index 2d19e072..4f4ff420 100644 --- a/learning/utils/dict_utils.py +++ b/learning/utils/dict_utils.py @@ -23,16 +23,14 @@ def compute_MC_returns(data: TensorDict, gamma, critic=None): @torch.no_grad -def compute_generalized_advantages(data, gamma, lam, critic, last_values=None): - data.update({"values": critic.evaluate(data["critic_obs"])}) - - data.update({"advantages": torch.zeros_like(data["values"])}) - +def compute_generalized_advantages(data, gamma, lam, critic): + last_values = critic.evaluate(data["next_critic_obs"][-1]) + advantages = torch.zeros_like(data["values"]) if last_values is not None: # todo check this # since we don't have observations for the last step, need last value plugged in not_done = ~data["dones"][-1] - data["advantages"][-1] = ( + advantages[-1] = ( data["rewards"][-1] + gamma * data["values"][-1] * data["timed_out"][-1] + gamma * last_values * not_done @@ -47,15 +45,14 @@ def compute_generalized_advantages(data, gamma, lam, critic, last_values=None): + gamma * data["values"][k + 1] * not_done - data["values"][k] ) - data["advantages"][k] = ( - td_error + gamma * lam * not_done * data["advantages"][k + 1] - ) + advantages[k] = td_error + gamma * lam * not_done * advantages[k + 1] - data["returns"] = data["advantages"] + data["values"] + return normalize(advantages) - data["advantages"] = (data["advantages"] - data["advantages"].mean()) / ( - data["advantages"].std() + 1e-8 - ) + +@torch.no_grad +def normalize(input, eps=1e-8): + return (input - input.mean()) / (input.std() + eps) # todo change num_epochs to num_batches From 098989a175231ca70a814230c1adf9ca5aabbb9b Mon Sep 17 00:00:00 2001 From: Steve Heim Date: Tue, 6 Aug 2024 13:19:37 -0400 Subject: [PATCH 12/77] fix hips_forward dimensionality --- gym/envs/mit_humanoid/mit_humanoid.py | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/gym/envs/mit_humanoid/mit_humanoid.py b/gym/envs/mit_humanoid/mit_humanoid.py index 118ac170..265f0ff9 100644 --- a/gym/envs/mit_humanoid/mit_humanoid.py +++ b/gym/envs/mit_humanoid/mit_humanoid.py @@ -223,12 +223,12 @@ def _reward_walk_freq(self): def _reward_hips_forward(self): # reward hip motors for pointing forward - hip_yaw_abad = torch.stack((self.dof_pos[:, 0:2], self.dof_pos[:, 5:7]), dim=1) - hip_yaw_abad -= torch.stack( + hip_yaw_abad = torch.cat((self.dof_pos[:, 0:2], self.dof_pos[:, 5:7]), dim=1) + hip_yaw_abad -= torch.cat( (self.default_dof_pos[:, 0:2], self.default_dof_pos[:, 5:7]), dim=1 ) - hip_yaw_abad /= torch.stack( - (self.scales["dof_pos"][0:2], self.scales["dof_pos"][5:7]), dim=1 + hip_yaw_abad /= torch.cat( + (self.scales["dof_pos"][0:2], self.scales["dof_pos"][5:7]) ) return (hip_yaw_abad).pow(2).mean(dim=1) # return self._sqrdexp(hip_yaw_abad).sum(dim=1).mean(dim=1) From 0611bcd52ba326d8a23b929a679382a72f14b0f9 Mon Sep 17 00:00:00 2001 From: Steve Heim Date: Tue, 6 Aug 2024 13:43:10 -0400 Subject: [PATCH 13/77] lander env --- gym/envs/__init__.py | 6 +- gym/envs/mit_humanoid/lander.py | 195 +++++++++++++++ gym/envs/mit_humanoid/lander_config.py | 331 +++++++++++++++++++++++++ 3 files changed, 531 insertions(+), 1 deletion(-) create mode 100644 gym/envs/mit_humanoid/lander.py create mode 100644 gym/envs/mit_humanoid/lander_config.py diff --git a/gym/envs/__init__.py b/gym/envs/__init__.py index f9d8b788..26710fce 100644 --- a/gym/envs/__init__.py +++ b/gym/envs/__init__.py @@ -19,7 +19,8 @@ "Anymal": ".anymal_c.anymal", "A1": ".a1.a1", "HumanoidRunning": ".mit_humanoid.humanoid_running", - "Pendulum": ".pendulum.pendulum" + "Pendulum": ".pendulum.pendulum", + "Lander": ".mit_humanoid.lander", } config_dict = { @@ -34,6 +35,7 @@ "HumanoidRunningCfg": ".mit_humanoid.humanoid_running_config", "PendulumCfg": ".pendulum.pendulum_config", "PendulumSACCfg": ".pendulum.pendulum_SAC_config", + "LanderCfg": ".mit_humanoid.lander_config", } runner_config_dict = { @@ -48,6 +50,7 @@ "HumanoidRunningRunnerCfg": ".mit_humanoid.humanoid_running_config", "PendulumRunnerCfg": ".pendulum.pendulum_config", "PendulumSACRunnerCfg": ".pendulum.pendulum_SAC_config", + "LanderRunnerCfg": ".mit_humanoid.lander_config", } task_dict = { @@ -77,6 +80,7 @@ "flat_anymal_c": ["Anymal", "AnymalCFlatCfg", "AnymalCFlatRunnerCfg"], "pendulum": ["Pendulum", "PendulumCfg", "PendulumRunnerCfg"], "sac_pendulum": ["Pendulum", "PendulumSACCfg", "PendulumSACRunnerCfg"], + "lander": ["Lander", "LanderCfg", "LanderRunnerCfg"], } for class_name, class_location in class_dict.items(): diff --git a/gym/envs/mit_humanoid/lander.py b/gym/envs/mit_humanoid/lander.py new file mode 100644 index 00000000..5856efb8 --- /dev/null +++ b/gym/envs/mit_humanoid/lander.py @@ -0,0 +1,195 @@ +import torch + +from gym.envs.base.legged_robot import LeggedRobot + +# from gym.envs.mit_humanoid.mit_humanoid import MIT_Humanoid +from .jacobian import _apply_coupling + + +class Lander(LeggedRobot): + def __init__(self, gym, sim, cfg, sim_params, sim_device, headless): + super().__init__(gym, sim, cfg, sim_params, sim_device, headless) + + def _init_buffers(self): + super()._init_buffers() + self._init_sampled_history_buffers() + + def _init_sampled_history_buffers(self): + self.sampled_history_dof_pos_target = torch.zeros( + (self.num_envs, self.num_dof * self.cfg.env.sampled_history_length), + device=self.device, + ) + self.sampled_history_dof_pos = torch.zeros( + self.num_envs, + self.num_dof * self.cfg.env.sampled_history_length, + device=self.device, + ) + self.sampled_history_dof_vel = torch.zeros( + self.num_envs, + self.num_dof * self.cfg.env.sampled_history_length, + device=self.device, + ) + self.sampled_history_counter = torch.zeros( + self.num_envs, dtype=int, device=self.device + ) + self.sampled_history_threshold = int( + self.cfg.control.ctrl_frequency / self.cfg.env.sampled_history_frequency + ) + + def _reset_system(self, env_ids): + if len(env_ids) == 0: + return + super()._reset_system(env_ids) + + self._reset_sampled_history_buffers(env_ids) + return + + def _reset_sampled_history_buffers(self, ids): + n = self.cfg.env.sampled_history_length + self.sampled_history_dof_pos_target[ids] = self.dof_pos_target[ids].tile(n) + self.sampled_history_dof_pos[ids] = self.dof_pos_target[ids].tile(n) + self.sampled_history_dof_vel[ids] = self.dof_pos_target[ids].tile(n) + + # compute_torques accounting for coupling, and filtering torques + def _compute_torques(self): + torques = _apply_coupling( + self.dof_pos, + self.dof_vel, + self.dof_pos_target + self.default_dof_pos, + self.dof_vel_target, + self.p_gains, + self.d_gains, + self.tau_ff, + ) + torques = torques.clip(-self.torque_limits, self.torque_limits) + return torques + + def _post_decimation_step(self): + super()._post_decimation_step() + self._update_sampled_history_buffers() + + def _update_sampled_history_buffers(self): + self.sampled_history_counter += 1 + + ids = torch.nonzero( + self.sampled_history_counter == self.sampled_history_threshold, + as_tuple=False, + ).flatten() + + self.sampled_history_dof_pos_target[ids] = torch.roll( + self.sampled_history_dof_pos_target[ids], self.num_dof, dims=1 + ) # check + self.sampled_history_dof_pos_target[ids, : self.num_dof] = self.dof_pos_target[ + ids + ] + self.sampled_history_dof_pos[ids] = torch.roll( + self.sampled_history_dof_pos[ids], self.num_dof, dims=1 + ) # check + self.sampled_history_dof_pos[ids, : self.num_dof] = self.dof_pos_target[ids] + self.sampled_history_dof_vel[ids] = torch.roll( + self.sampled_history_dof_vel[ids], self.num_dof, dims=1 + ) # check + self.sampled_history_dof_vel[ids, : self.num_dof] = self.dof_pos_target[ids] + + self.sampled_history_counter[ids] = 0 + + # --- rewards --- + + def _switch(self, mode=None): + c_vel = torch.linalg.norm(self.commands, dim=1) + switch = torch.exp( + -torch.square( + torch.max( + torch.zeros_like(c_vel), + c_vel - self.cfg.reward_settings.switch_scale, + ) + ) + / self.cfg.reward_settings.switch_scale + ) + if mode is None or mode == "stand": + return switch + elif mode == "move": + return 1 - switch + + def _reward_lin_vel_xy(self): + return torch.exp( + -torch.linalg.norm(self.commands[:, :2] - self.base_lin_vel[:, :2], dim=1) + ) + + def _reward_lin_vel_z(self): + # Penalize z axis base linear velocity w. squared exp + return self._sqrdexp(self.base_lin_vel[:, 2] / self.scales["base_lin_vel"]) + + def _reward_orientation(self): + # Penalize non flat base orientation + return torch.sum( + self._sqrdexp(torch.square(self.projected_gravity[:, :2])), dim=1 + ) + + def _reward_min_base_height(self): + """Squared exponential saturating at base_height target""" + error = self.base_height - self.cfg.reward_settings.base_height_target + error = torch.clamp(error, max=0, min=None).flatten() + return self._sqrdexp(error) + + def _reward_tracking_lin_vel(self): + """Tracking of linear velocity commands (xy axes)""" + # just use lin_vel? + error = self.commands[:, :2] - self.base_lin_vel[:, :2] + # * scale by (1+|cmd|): if cmd=0, no scaling. + error *= 1.0 / (1.0 + torch.abs(self.commands[:, :2])) + return torch.mean(self._sqrdexp(error), dim=1) + + def _reward_dof_vel(self): + # Penalize dof velocities + return torch.mean( + self._sqrdexp(self.dof_vel / self.scales["dof_vel"]), dim=1 + ) * self._switch("stand") + + def _reward_dof_near_home(self): + return self._sqrdexp( + (self.dof_pos - self.default_dof_pos) / self.scales["dof_pos"] + ).mean(dim=1) + + def _reward_stand_still(self): + """Penalize motion at zero commands""" + # * normalize angles so we care about being within 5 deg + rew_pos = torch.mean( + self._sqrdexp((self.dof_pos - self.default_dof_pos) / torch.pi * 36), dim=1 + ) + rew_vel = torch.mean(self._sqrdexp(self.dof_vel), dim=1) + rew_base_vel = torch.mean(torch.square(self.base_lin_vel), dim=1) + rew_base_vel += torch.mean(torch.square(self.base_ang_vel), dim=1) + return rew_vel + rew_pos - rew_base_vel * self._switch("stand") + + def _compute_grf(self, grf_norm=True): + grf = torch.norm(self.contact_forces[:, self.feet_indices, :], dim=-1) + if grf_norm: + return torch.clamp_max(grf / self.cfg.asset.total_mass, 1.0) + else: + return grf + + def smooth_sqr_wave(self, phase, sigma=0.2): # sigma=0 is step function + return phase.sin() / (2 * torch.sqrt(phase.sin() ** 2.0 + sigma**2.0)) + 0.5 + + def _reward_walk_freq(self): + # Penalize deviation from base frequency + return torch.mean( + self._sqrdexp( + (self.oscillator_freq - self.cfg.oscillator.base_frequency) + / self.cfg.oscillator.base_frequency + ), + dim=1, + ) * self._switch("move") + + def _reward_hips_forward(self): + # reward hip motors for pointing forward + hip_yaw_abad = torch.stack((self.dof_pos[:, 0:2], self.dof_pos[:, 5:7]), dim=1) + hip_yaw_abad -= torch.stack( + (self.default_dof_pos[:, 0:2], self.default_dof_pos[:, 5:7]), dim=1 + ) + hip_yaw_abad /= torch.stack( + (self.scales["dof_pos"][0:2], self.scales["dof_pos"][5:7]), dim=1 + ) + return (hip_yaw_abad).pow(2).mean(dim=1) + # return self._sqrdexp(hip_yaw_abad).sum(dim=1).mean(dim=1) diff --git a/gym/envs/mit_humanoid/lander_config.py b/gym/envs/mit_humanoid/lander_config.py new file mode 100644 index 00000000..a4262f0d --- /dev/null +++ b/gym/envs/mit_humanoid/lander_config.py @@ -0,0 +1,331 @@ +from gym.envs.base.legged_robot_config import ( + LeggedRobotCfg, + LeggedRobotRunnerCfg, +) + +BASE_HEIGHT_REF = 0.80 + + +class LanderCfg(LeggedRobotCfg): + class env(LeggedRobotCfg.env): + num_envs = 4096 + num_actuators = 18 + episode_length_s = 5 # episode length in seconds + + sampled_history_length = 3 # n samples + sampled_history_frequency = 10 # [Hz] + + class terrain(LeggedRobotCfg.terrain): + pass + + class init_state(LeggedRobotCfg.init_state): + # * default setup chooses how the initial conditions are chosen. + # * "reset_to_basic" = a single position with added randomized noise. + # * "reset_to_range" = a range of joint positions and velocities. + # * "reset_to_traj" = feed in a trajectory to sample from. + reset_mode = "reset_to_range" + + default_joint_angles = { + "hip_yaw": 0.0, + "hip_abad": 0.0, + "hip_pitch": -0.667751, + "knee": 1.4087, + "ankle": -0.708876, + "shoulder_pitch": 0.0, + "shoulder_abad": 0.0, + "shoulder_yaw": 0.0, + "elbow": -1.25, + } + + # * default COM for basic initialization + pos = [0.0, 0.0, 0.6] # x,y,z [m] + rot = [0.0, 0.0, 0.0, 1.0] # x,y,z,w [quat] + lin_vel = [0.0, 0.0, 0.0] # x,y,z [m/s] + ang_vel = [0.0, 0.0, 0.0] # x,y,z [rad/s] + + # * initialization for random range setup + + dof_pos_range = { + "hip_yaw": [-0.0, 0.0], + "hip_abad": [-0.0, 0.0], + "hip_pitch": [-0.667751, -0.667751], + "knee": [1.4087, 1.4087], + "ankle": [-0.708876, -0.708876], + "shoulder_pitch": [0.0, 0.0], + "shoulder_abad": [0.0, 0.0], + "shoulder_yaw": [0.0, 0.0], + "elbow": [0, 0], + } + dof_vel_range = { + "hip_yaw": [-0.1, 0.1], + "hip_abad": [-0.1, 0.1], + "hip_pitch": [-0.1, 0.1], + "knee": [-0.1, 0.1], + "ankle": [-0.1, 0.1], + "shoulder_pitch": [0.0, 0.0], + "shoulder_abad": [0.0, 0.0], + "shoulder_yaw": [0.0, 0.0], + "elbow": [0.0, 0.0], + } + + root_pos_range = [ + [0.0, 0.0], # x + [0.0, 0.0], # y + [1.0, 1.5], # z + [-0.1, 0.1], # roll + [-0.1, 0.1], # pitch + [-0.1, 0.1], + ] # yaw + + root_vel_range = [ + [-0.75, 2.75], # x + [-0.55, 0.55], # y + [-2.5, 0.25], # z + [-0.35, 0.35], # roll + [-0.35, 0.35], # pitch + [-0.35, 0.35], # yaw + ] + + class control(LeggedRobotCfg.control): + # * PD Drive parameters: + stiffness = { + "hip_yaw": 30.0, + "hip_abad": 30.0, + "hip_pitch": 30.0, + "knee": 30.0, + "ankle": 30.0, + "shoulder_pitch": 40.0, + "shoulder_abad": 40.0, + "shoulder_yaw": 40.0, + "elbow": 50.0, + } # [N*m/rad] + damping = { + "hip_yaw": 2.0, + "hip_abad": 2.0, + "hip_pitch": 2.0, + "knee": 2.0, + "ankle": 2.0, + "shoulder_pitch": 2.0, + "shoulder_abad": 2.0, + "shoulder_yaw": 2.0, + "elbow": 1.0, + } # [N*m*s/rad] + + ctrl_frequency = 100 + desired_sim_frequency = 1000 + + # class oscillator: + # base_frequency = 3.0 # [Hz] + + class commands: + resampling_time = 10.0 # time before command are changed[s] + + class ranges: + lin_vel_x = [-0.0, 0.0] # min max [m/s] [-0.75, 0.75] + lin_vel_y = 0.0 # max [m/s] + yaw_vel = 0.0 # max [rad/s] + + class push_robots: + toggle = True + interval_s = 1 + max_push_vel_xy = 0.5 + push_box_dims = [0.1, 0.1, 0.3] # x,y,z [m] + + class domain_rand: + randomize_friction = True + friction_range = [0.5, 1.25] + randomize_base_mass = True + added_mass_range = [-1.0, 1.0] + + class asset(LeggedRobotCfg.asset): + file = ( + "{LEGGED_GYM_ROOT_DIR}/resources/robots/" + + "mit_humanoid/urdf/humanoid_F_sf_learnt.urdf" + ) + # foot_collisionbox_names = ["foot"] + foot_name = "foot" + penalize_contacts_on = ["arm"] + terminate_after_contacts_on = ["base"] + end_effector_names = ["hand", "foot"] # ?? + flip_visual_attachments = False + self_collisions = 0 # 1 to disagble, 0 to enable...bitwise filter + collapse_fixed_joints = False + # * see GymDofDriveModeFlags + # * (0 is none, 1 is pos tgt, 2 is vel tgt, 3 effort) + default_dof_drive_mode = 3 + fix_base_link = False + disable_gravity = False + disable_motors = False + total_mass = 25.0 + + class reward_settings(LeggedRobotCfg.reward_settings): + soft_dof_pos_limit = 0.8 + soft_dof_vel_limit = 0.8 + soft_torque_limit = 0.8 + max_contact_force = 1500.0 + base_height_target = BASE_HEIGHT_REF + tracking_sigma = 0.25 + + # a smooth switch based on |cmd| (commanded velocity). + switch_scale = 0.5 + switch_threshold = 0.2 + + class scaling(LeggedRobotCfg.scaling): + base_ang_vel = 2.5 + base_lin_vel = 1.5 + commands = 1 + base_height = BASE_HEIGHT_REF + dof_pos = [ + 0.1, + 0.2, + 0.8, + 0.8, + 0.8, + 0.1, + 0.2, + 0.8, + 0.8, + 0.8, + 0.1, + 0.1, + 0.1, + 0.1, + 0.1, + 0.1, + 0.1, + 0.1, + ] + # # * Action scales + dof_pos_target = dof_pos + dof_vel = [ + 0.5, + 1.0, + 4.0, + 4.0, + 2.0, + 0.5, + 1.0, + 4.0, + 4.0, + 2.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + ] + dof_pos_history = 3 * dof_pos + + +class LanderRunnerCfg(LeggedRobotRunnerCfg): + seed = -1 + runner_class_name = "OnPolicyRunner" + + class actor(LeggedRobotRunnerCfg.actor): + init_noise_std = 1.0 + hidden_dims = [512, 256, 128] + # * can be elu, relu, selu, crelu, lrelu, tanh, sigmoid + activation = "elu" + smooth_exploration = False + + obs = [ + "base_height", + "base_lin_vel", + "base_ang_vel", + "projected_gravity", + # "commands", + "dof_pos_obs", + "dof_vel", + "dof_pos_history", + # "sampled_history_dof_pos", + # "sampled_history_dof_vel", + # "sampled_history_dof_pos_target", + # "oscillator_obs", + ] + normalize_obs = True + + actions = ["dof_pos_target"] + disable_actions = False + + class noise: + dof_pos = 0.005 + dof_vel = 0.05 + base_ang_vel = 0.025 + base_lin_vel = 0.025 + projected_gravity = 0.01 + feet_contact_state = 0.025 + + class critic(LeggedRobotRunnerCfg.critic): + hidden_dims = [512, 256, 128] + # * can be elu, relu, selu, crelu, lrelu, tanh, sigmoid + activation = "elu" + + obs = [ + "base_height", + "base_lin_vel", + "base_ang_vel", + "projected_gravity", + "commands", + "dof_pos_obs", + "dof_vel", + "dof_pos_history", + ] + normalize_obs = True + + class reward: + class weights: + # tracking_lin_vel = 0.0 + # tracking_ang_vel = 1.5 + # orientation = 1.0 + torques = 5.0e-4 + # min_base_height = 1.5 + action_rate = 1e-3 + action_rate2 = 1e-3 + lin_vel_z = 0.0 + ang_vel_xy = 0.0 + # dof_vel = 0.25 + # stand_still = 0.25 + dof_pos_limits = 0.25 + dof_near_home = 0.25 + # stance = 1.0 + # swing = 1.0 + hips_forward = 0.0 + # walk_freq = 0.0 # 2.5 + + class termination_weight: + termination = 15 + + class algorithm(LeggedRobotRunnerCfg.algorithm): + # both + gamma = 0.99 + lam = 0.95 + # shared + batch_size = 2**15 + max_gradient_steps = 24 + # new + storage_size = 2**17 # new + batch_size = 2**15 # new + + clip_param = 0.2 + learning_rate = 1.0e-3 + max_grad_norm = 1.0 + # Critic + use_clipped_value_loss = True + # Actor + entropy_coef = 0.01 + schedule = "adaptive" # could be adaptive, fixed + desired_kl = 0.01 + lr_range = [1e-5, 1e-2] + lr_ratio = 1.5 + + class runner(LeggedRobotRunnerCfg.runner): + policy_class_name = "ActorCritic" + algorithm_class_name = "PPO2" + num_steps_per_env = 24 + max_iterations = 1000 + run_name = "Standing" + experiment_name = "Humanoid" + save_interval = 50 From fd308656c2aeaf81e02c79c1d19c22c976ebf02b Mon Sep 17 00:00:00 2001 From: Steve Heim Date: Wed, 7 Aug 2024 14:17:00 -0400 Subject: [PATCH 14/77] unify humanoid and lander put jacobian into MIT_humanoid base environment, rather than import; importing breaks when running --original_config --- gym/envs/mit_humanoid/lander.py | 113 +++---------------------- gym/envs/mit_humanoid/lander_config.py | 36 ++++---- gym/envs/mit_humanoid/mit_humanoid.py | 99 ++++++++++------------ 3 files changed, 74 insertions(+), 174 deletions(-) diff --git a/gym/envs/mit_humanoid/lander.py b/gym/envs/mit_humanoid/lander.py index 5856efb8..972eb413 100644 --- a/gym/envs/mit_humanoid/lander.py +++ b/gym/envs/mit_humanoid/lander.py @@ -1,98 +1,13 @@ import torch -from gym.envs.base.legged_robot import LeggedRobot +# from gym.envs.base.legged_robot import LeggedRobot +from gym.envs.mit_humanoid.mit_humanoid import MIT_Humanoid -# from gym.envs.mit_humanoid.mit_humanoid import MIT_Humanoid -from .jacobian import _apply_coupling - -class Lander(LeggedRobot): +class Lander(MIT_Humanoid): def __init__(self, gym, sim, cfg, sim_params, sim_device, headless): super().__init__(gym, sim, cfg, sim_params, sim_device, headless) - def _init_buffers(self): - super()._init_buffers() - self._init_sampled_history_buffers() - - def _init_sampled_history_buffers(self): - self.sampled_history_dof_pos_target = torch.zeros( - (self.num_envs, self.num_dof * self.cfg.env.sampled_history_length), - device=self.device, - ) - self.sampled_history_dof_pos = torch.zeros( - self.num_envs, - self.num_dof * self.cfg.env.sampled_history_length, - device=self.device, - ) - self.sampled_history_dof_vel = torch.zeros( - self.num_envs, - self.num_dof * self.cfg.env.sampled_history_length, - device=self.device, - ) - self.sampled_history_counter = torch.zeros( - self.num_envs, dtype=int, device=self.device - ) - self.sampled_history_threshold = int( - self.cfg.control.ctrl_frequency / self.cfg.env.sampled_history_frequency - ) - - def _reset_system(self, env_ids): - if len(env_ids) == 0: - return - super()._reset_system(env_ids) - - self._reset_sampled_history_buffers(env_ids) - return - - def _reset_sampled_history_buffers(self, ids): - n = self.cfg.env.sampled_history_length - self.sampled_history_dof_pos_target[ids] = self.dof_pos_target[ids].tile(n) - self.sampled_history_dof_pos[ids] = self.dof_pos_target[ids].tile(n) - self.sampled_history_dof_vel[ids] = self.dof_pos_target[ids].tile(n) - - # compute_torques accounting for coupling, and filtering torques - def _compute_torques(self): - torques = _apply_coupling( - self.dof_pos, - self.dof_vel, - self.dof_pos_target + self.default_dof_pos, - self.dof_vel_target, - self.p_gains, - self.d_gains, - self.tau_ff, - ) - torques = torques.clip(-self.torque_limits, self.torque_limits) - return torques - - def _post_decimation_step(self): - super()._post_decimation_step() - self._update_sampled_history_buffers() - - def _update_sampled_history_buffers(self): - self.sampled_history_counter += 1 - - ids = torch.nonzero( - self.sampled_history_counter == self.sampled_history_threshold, - as_tuple=False, - ).flatten() - - self.sampled_history_dof_pos_target[ids] = torch.roll( - self.sampled_history_dof_pos_target[ids], self.num_dof, dims=1 - ) # check - self.sampled_history_dof_pos_target[ids, : self.num_dof] = self.dof_pos_target[ - ids - ] - self.sampled_history_dof_pos[ids] = torch.roll( - self.sampled_history_dof_pos[ids], self.num_dof, dims=1 - ) # check - self.sampled_history_dof_pos[ids, : self.num_dof] = self.dof_pos_target[ids] - self.sampled_history_dof_vel[ids] = torch.roll( - self.sampled_history_dof_vel[ids], self.num_dof, dims=1 - ) # check - self.sampled_history_dof_vel[ids, : self.num_dof] = self.dof_pos_target[ids] - - self.sampled_history_counter[ids] = 0 - # --- rewards --- def _switch(self, mode=None): @@ -172,24 +87,18 @@ def _compute_grf(self, grf_norm=True): def smooth_sqr_wave(self, phase, sigma=0.2): # sigma=0 is step function return phase.sin() / (2 * torch.sqrt(phase.sin() ** 2.0 + sigma**2.0)) + 0.5 - def _reward_walk_freq(self): - # Penalize deviation from base frequency - return torch.mean( - self._sqrdexp( - (self.oscillator_freq - self.cfg.oscillator.base_frequency) - / self.cfg.oscillator.base_frequency - ), - dim=1, - ) * self._switch("move") - def _reward_hips_forward(self): # reward hip motors for pointing forward - hip_yaw_abad = torch.stack((self.dof_pos[:, 0:2], self.dof_pos[:, 5:7]), dim=1) - hip_yaw_abad -= torch.stack( + hip_yaw_abad = torch.cat((self.dof_pos[:, 0:2], self.dof_pos[:, 5:7]), dim=1) + hip_yaw_abad -= torch.cat( (self.default_dof_pos[:, 0:2], self.default_dof_pos[:, 5:7]), dim=1 ) - hip_yaw_abad /= torch.stack( - (self.scales["dof_pos"][0:2], self.scales["dof_pos"][5:7]), dim=1 + hip_yaw_abad /= torch.cat( + (self.scales["dof_pos"][0:2], self.scales["dof_pos"][5:7]) ) return (hip_yaw_abad).pow(2).mean(dim=1) # return self._sqrdexp(hip_yaw_abad).sum(dim=1).mean(dim=1) + + def _reward_power(self): + power = self.torques * self.dof_vel + return power.pow(2).mean(dim=1) diff --git a/gym/envs/mit_humanoid/lander_config.py b/gym/envs/mit_humanoid/lander_config.py index a4262f0d..0de34b4c 100644 --- a/gym/envs/mit_humanoid/lander_config.py +++ b/gym/envs/mit_humanoid/lander_config.py @@ -10,7 +10,7 @@ class LanderCfg(LeggedRobotCfg): class env(LeggedRobotCfg.env): num_envs = 4096 num_actuators = 18 - episode_length_s = 5 # episode length in seconds + episode_length_s = 10 # episode length in seconds sampled_history_length = 3 # n samples sampled_history_frequency = 10 # [Hz] @@ -71,7 +71,7 @@ class init_state(LeggedRobotCfg.init_state): root_pos_range = [ [0.0, 0.0], # x [0.0, 0.0], # y - [1.0, 1.5], # z + [0.64, 1.5], # z [-0.1, 0.1], # roll [-0.1, 0.1], # pitch [-0.1, 0.1], @@ -144,7 +144,7 @@ class asset(LeggedRobotCfg.asset): ) # foot_collisionbox_names = ["foot"] foot_name = "foot" - penalize_contacts_on = ["arm"] + penalize_contacts_on = ["arm", "hand", "shoulder"] terminate_after_contacts_on = ["base"] end_effector_names = ["hand", "foot"] # ?? flip_visual_attachments = False @@ -228,7 +228,7 @@ class actor(LeggedRobotRunnerCfg.actor): init_noise_std = 1.0 hidden_dims = [512, 256, 128] # * can be elu, relu, selu, crelu, lrelu, tanh, sigmoid - activation = "elu" + activation = "tanh" smooth_exploration = False obs = [ @@ -236,16 +236,15 @@ class actor(LeggedRobotRunnerCfg.actor): "base_lin_vel", "base_ang_vel", "projected_gravity", - # "commands", + "commands", "dof_pos_obs", "dof_vel", "dof_pos_history", - # "sampled_history_dof_pos", - # "sampled_history_dof_vel", - # "sampled_history_dof_pos_target", - # "oscillator_obs", + "sampled_history_dof_pos", + "sampled_history_dof_vel", + "sampled_history_dof_pos_target", ] - normalize_obs = True + normalize_obs = False actions = ["dof_pos_target"] disable_actions = False @@ -261,10 +260,10 @@ class noise: class critic(LeggedRobotRunnerCfg.critic): hidden_dims = [512, 256, 128] # * can be elu, relu, selu, crelu, lrelu, tanh, sigmoid - activation = "elu" + activation = "tanh" obs = [ - "base_height", + # "base_height", "base_lin_vel", "base_ang_vel", "projected_gravity", @@ -272,21 +271,25 @@ class critic(LeggedRobotRunnerCfg.critic): "dof_pos_obs", "dof_vel", "dof_pos_history", + "sampled_history_dof_pos", + "sampled_history_dof_vel", + "sampled_history_dof_pos_target", ] - normalize_obs = True + normalize_obs = False class reward: class weights: # tracking_lin_vel = 0.0 # tracking_ang_vel = 1.5 # orientation = 1.0 - torques = 5.0e-4 - # min_base_height = 1.5 + torques = 5.0e-5 + power = 0 # 1.0e-2 + min_base_height = 1.5 action_rate = 1e-3 action_rate2 = 1e-3 lin_vel_z = 0.0 ang_vel_xy = 0.0 - # dof_vel = 0.25 + dof_vel = 0.5 # stand_still = 0.25 dof_pos_limits = 0.25 dof_near_home = 0.25 @@ -294,6 +297,7 @@ class weights: # swing = 1.0 hips_forward = 0.0 # walk_freq = 0.0 # 2.5 + collision = 1.0 class termination_weight: termination = 15 diff --git a/gym/envs/mit_humanoid/mit_humanoid.py b/gym/envs/mit_humanoid/mit_humanoid.py index 265f0ff9..a580fd57 100644 --- a/gym/envs/mit_humanoid/mit_humanoid.py +++ b/gym/envs/mit_humanoid/mit_humanoid.py @@ -1,8 +1,6 @@ import torch from gym.envs.base.legged_robot import LeggedRobot -from .jacobian import _apply_coupling -from gym.utils import exp_avg_filter class MIT_Humanoid(LeggedRobot): @@ -11,9 +9,6 @@ def __init__(self, gym, sim, cfg, sim_params, sim_device, headless): def _init_buffers(self): super()._init_buffers() - self.oscillators = torch.zeros(self.num_envs, 2, device=self.device) - self.oscillator_obs = torch.zeros(self.num_envs, 4, device=self.device) - self.oscillator_freq = torch.zeros(self.num_envs, 2, device=self.device) self._init_sampled_history_buffers() def _init_sampled_history_buffers(self): @@ -38,22 +33,51 @@ def _init_sampled_history_buffers(self): self.cfg.control.ctrl_frequency / self.cfg.env.sampled_history_frequency ) + def _apply_coupling(self, q, qd, q_des, qd_des, kp, kd, tau_ff): + # Create a Jacobian matrix and move it to the same device as input tensors + J = torch.eye(q.shape[-1]).to(q.device) + J[4, 3] = 1 + J[9, 8] = 1 + + # Perform transformations using Jacobian + q = torch.matmul(q, J.T) + qd = torch.matmul(qd, J.T) + q_des = torch.matmul(q_des, J.T) + qd_des = torch.matmul(qd_des, J.T) + + # Inverse of the transpose of Jacobian + J_inv_T = torch.inverse(J.T) + + # Compute feed-forward torques + tau_ff = torch.matmul(J_inv_T, tau_ff.T).T + + # Compute kp and kd terms + kp = torch.diagonal( + torch.matmul( + torch.matmul(J_inv_T, torch.diag_embed(kp, dim1=-2, dim2=-1)), J_inv_T.T + ), + dim1=-2, + dim2=-1, + ) + + kd = torch.diagonal( + torch.matmul( + torch.matmul(J_inv_T, torch.diag_embed(kd, dim1=-2, dim2=-1)), J_inv_T.T + ), + dim1=-2, + dim2=-1, + ) + + # Compute torques + torques = kp * (q_des - q) + kd * (qd_des - qd) + tau_ff + torques = torch.matmul(torques, J) + + return torques + def _reset_system(self, env_ids): if len(env_ids) == 0: return super()._reset_system(env_ids) - # reset oscillators, with a pi phase shift between left and right - self.oscillators[env_ids, 0] = ( - torch.rand(len(env_ids), device=self.device) * 2 * torch.pi - ) - self.oscillators[env_ids, 1] = self.oscillators[env_ids, 0] + torch.pi - self.oscillators = torch.remainder(self.oscillators, 2 * torch.pi) - # reset oscillator velocities to base freq - self.oscillator_freq[env_ids] = self.cfg.oscillator.base_frequency - # recompute oscillator observations - self.oscillator_obs = torch.cat( - (self.oscillators.cos(), self.oscillators.sin()), dim=1 - ) self._reset_sampled_history_buffers(env_ids) return @@ -65,7 +89,7 @@ def _reset_sampled_history_buffers(self, ids): # compute_torques accounting for coupling, and filtering torques def _compute_torques(self): - torques = _apply_coupling( + torques = self._apply_coupling( self.dof_pos, self.dof_vel, self.dof_pos_target + self.default_dof_pos, @@ -74,25 +98,12 @@ def _compute_torques(self): self.d_gains, self.tau_ff, ) - torques = torques.clip(-self.torque_limits, self.torque_limits) - return exp_avg_filter(torques, self.torques, self.cfg.control.filter_gain) - - # oscillator integration + return torques.clip(-self.torque_limits, self.torque_limits) def _post_decimation_step(self): super()._post_decimation_step() - self._step_oscillators() self._update_sampled_history_buffers() - def _step_oscillators(self, dt=None): - if dt is None: - dt = self.dt - self.oscillators += (self.oscillator_freq * 2 * torch.pi) * dt - self.oscillators = torch.remainder(self.oscillators, 2 * torch.pi) - self.oscillator_obs = torch.cat( - (torch.cos(self.oscillators), torch.sin(self.oscillators)), dim=1 - ) - def _update_sampled_history_buffers(self): self.sampled_history_counter += 1 @@ -187,20 +198,6 @@ def _reward_stand_still(self): rew_base_vel += torch.mean(torch.square(self.base_ang_vel), dim=1) return rew_vel + rew_pos - rew_base_vel * self._switch("stand") - def _reward_stance(self): - # phase = torch.maximum( - # torch.zeros_like(self.oscillators), -self.oscillators.sin() - # ) # positive during swing, negative during stance - phase = self.smooth_sqr_wave(self.oscillators) - return (phase * self._compute_grf()).mean(dim=1) - - def _reward_swing(self): - # phase = torch.maximum( - # torch.zeros_like(self.oscillators), self.oscillators.sin() - # ) # positive during swing, negative during stance - phase = self.smooth_sqr_wave(self.oscillators + torch.pi) - return -(phase * self._compute_grf()).mean(dim=1) - def _compute_grf(self, grf_norm=True): grf = torch.norm(self.contact_forces[:, self.feet_indices, :], dim=-1) if grf_norm: @@ -211,16 +208,6 @@ def _compute_grf(self, grf_norm=True): def smooth_sqr_wave(self, phase, sigma=0.2): # sigma=0 is step function return phase.sin() / (2 * torch.sqrt(phase.sin() ** 2.0 + sigma**2.0)) + 0.5 - def _reward_walk_freq(self): - # Penalize deviation from base frequency - return torch.mean( - self._sqrdexp( - (self.oscillator_freq - self.cfg.oscillator.base_frequency) - / self.cfg.oscillator.base_frequency - ), - dim=1, - ) * self._switch("move") - def _reward_hips_forward(self): # reward hip motors for pointing forward hip_yaw_abad = torch.cat((self.dof_pos[:, 0:2], self.dof_pos[:, 5:7]), dim=1) From 6248559e8229abc2daba833966dfaa9e1786a5b7 Mon Sep 17 00:00:00 2001 From: Steve Heim Date: Tue, 27 Aug 2024 19:05:11 -0400 Subject: [PATCH 15/77] everything integrated and works --- gym/envs/__init__.py | 2 +- gym/envs/base/fixed_robot.py | 21 +- gym/envs/base/fixed_robot_config.py | 2 +- gym/envs/base/legged_robot.py | 9 +- gym/envs/base/legged_robot_config.py | 10 +- gym/envs/base/task_skeleton.py | 1 + gym/envs/cartpole/cartpole_config.py | 22 +- gym/envs/mini_cheetah/mini_cheetah_config.py | 3 +- .../mini_cheetah/mini_cheetah_osc_config.py | 71 +- .../mini_cheetah/mini_cheetah_ref_config.py | 12 +- gym/envs/pendulum/pendulum.py | 43 +- gym/envs/pendulum/pendulum_config.py | 2 +- gym/utils/helpers.py | 2 +- learning/__init__.py | 6 + learning/algorithms/__init__.py | 2 +- learning/modules/QRCritics.py | 849 ++++++++++++++++++ learning/modules/__init__.py | 1 + learning/modules/critic.py | 3 +- learning/modules/utils/neural_net.py | 91 +- learning/runners/BaseRunner.py | 3 + learning/runners/__init__.py | 4 +- learning/runners/custom_critic_runner.py | 178 ++++ learning/runners/datalogging_runner.py | 213 +++++ learning/runners/my_runner.py | 55 +- learning/runners/on_policy_runner.py | 5 +- learning/utils/dict_utils.py | 43 +- requirements.txt | 3 +- 27 files changed, 1503 insertions(+), 153 deletions(-) create mode 100644 learning/modules/QRCritics.py create mode 100644 learning/runners/custom_critic_runner.py create mode 100644 learning/runners/datalogging_runner.py diff --git a/gym/envs/__init__.py b/gym/envs/__init__.py index f9d8b788..1b78a261 100644 --- a/gym/envs/__init__.py +++ b/gym/envs/__init__.py @@ -19,7 +19,7 @@ "Anymal": ".anymal_c.anymal", "A1": ".a1.a1", "HumanoidRunning": ".mit_humanoid.humanoid_running", - "Pendulum": ".pendulum.pendulum" + "Pendulum": ".pendulum.pendulum", } config_dict = { diff --git a/gym/envs/base/fixed_robot.py b/gym/envs/base/fixed_robot.py index c3cf0566..544eba52 100644 --- a/gym/envs/base/fixed_robot.py +++ b/gym/envs/base/fixed_robot.py @@ -79,7 +79,7 @@ def _step_physx_sim(self): self.num_envs, device=self.device ) self.gym.set_dof_actuation_force_tensor( - self.sim, gymtorch.unwrap_tensor(self.torques) + self.sim, gymtorch.unwrap_tensor(torques_to_gym_tensor) ) self.gym.simulate(self.sim) if self.device == "cpu": @@ -184,10 +184,7 @@ def _process_dof_props(self, props, env_id): try: # ! this seems like a crime... self.torque_limits[i] = props["effort"][i].item() except: - print( - "WARNING: passive joints need to be listed after " - + "active joints in the URDF." - ) + print("WARNING: your system has unactuated joints") # * soft limits m = (self.dof_pos_limits[i, 0] + self.dof_pos_limits[i, 1]) / 2 r = self.dof_pos_limits[i, 1] - self.dof_pos_limits[i, 0] @@ -399,10 +396,7 @@ def _init_buffers(self): self.default_act_pos = self.default_act_pos.unsqueeze(0) # * store indices of actuated joints self.act_idx = to_torch(actuated_idx, dtype=torch.long, device=self.device) - # * check that init range highs and lows are consistent - # * and repopulate to match - if self.cfg.init_state.reset_mode == "reset_to_range": - self.initialize_ranges_for_initial_conditions() + self.initialize_ranges_for_initial_conditions() def initialize_ranges_for_initial_conditions(self): self.dof_pos_range = torch.zeros( @@ -568,11 +562,12 @@ def _get_env_origins(self): # TODO: do without terrain # ------------ reward functions---------------- - def _sqrdexp(self, x, scale=1.0): + def _sqrdexp(self, x, sigma=None): """shorthand helper for squared exponential""" - return torch.exp( - -torch.square(x / scale) / self.cfg.reward_settings.tracking_sigma - ) + if sigma is None: + return torch.exp(-torch.square(x) / self.cfg.reward_settings.tracking_sigma) + else: + return torch.exp(-torch.square(x) / sigma) def _reward_torques(self): """Penalize torques""" diff --git a/gym/envs/base/fixed_robot_config.py b/gym/envs/base/fixed_robot_config.py index 7a19f2e4..60160b9c 100644 --- a/gym/envs/base/fixed_robot_config.py +++ b/gym/envs/base/fixed_robot_config.py @@ -169,7 +169,7 @@ class algorithm: lam = 0.95 # shared batch_size = 2**15 - max_gradient_steps = 10 + max_gradient_steps = 24 # new storage_size = 2**17 # new batch_size = 2**15 # new diff --git a/gym/envs/base/legged_robot.py b/gym/envs/base/legged_robot.py index e02507d5..9923977d 100644 --- a/gym/envs/base/legged_robot.py +++ b/gym/envs/base/legged_robot.py @@ -980,11 +980,12 @@ def _get_heights(self, env_ids=None): return heights.view(self.num_envs, -1) * self.terrain.cfg.vertical_scale - def _sqrdexp(self, x, scale=1.0): + def _sqrdexp(self, x, sigma=None): """shorthand helper for squared exponential""" - return torch.exp( - -torch.square(x / scale) / self.cfg.reward_settings.tracking_sigma - ) + if sigma is None: + return torch.exp(-torch.square(x) / self.cfg.reward_settings.tracking_sigma) + else: + return torch.exp(-torch.square(x) / sigma) # ------------ reward functions---------------- diff --git a/gym/envs/base/legged_robot_config.py b/gym/envs/base/legged_robot_config.py index 7dd59807..0b9cf7a4 100644 --- a/gym/envs/base/legged_robot_config.py +++ b/gym/envs/base/legged_robot_config.py @@ -246,7 +246,11 @@ class actor: "observation_b", "these_need_to_be_atributes_(states)_of_the_robot_env", ] - normalize_obs = True + critic_obs = [ + "observation_x", + "observation_y", + "critic_obs_can_be_the_same_or_different_than_actor_obs", + ] actions = ["q_des"] disable_actions = False @@ -309,8 +313,8 @@ class algorithm: entropy_coef = 0.01 schedule = "adaptive" # could be adaptive, fixed desired_kl = 0.01 - lr_range = [2e-4, 1e-2] - lr_ratio = 1.3 + lr_range = [2e-5, 1e-2] + lr_ratio = 1.5 class runner: policy_class_name = "ActorCritic" diff --git a/gym/envs/base/task_skeleton.py b/gym/envs/base/task_skeleton.py index 6a1b352c..0974970a 100644 --- a/gym/envs/base/task_skeleton.py +++ b/gym/envs/base/task_skeleton.py @@ -45,6 +45,7 @@ def reset(self): """Reset all robots""" self._reset_idx(torch.arange(self.num_envs, device=self.device)) self.step() + self.episode_length_buf[:] = 0 def _reset_buffers(self): self.to_be_reset[:] = False diff --git a/gym/envs/cartpole/cartpole_config.py b/gym/envs/cartpole/cartpole_config.py index 6e073d5c..c3489021 100644 --- a/gym/envs/cartpole/cartpole_config.py +++ b/gym/envs/cartpole/cartpole_config.py @@ -7,7 +7,7 @@ class CartpoleCfg(FixedRobotCfg): class env(FixedRobotCfg.env): num_envs = 4024 num_actuators = 1 # 1 for the cart force - episode_length_s = 20 + episode_length_s = 5.0 class terrain(FixedRobotCfg.terrain): pass @@ -35,7 +35,8 @@ class control(FixedRobotCfg.control): 1, # slider_to_cart 0, ] # cart_to_pole - + stiffness = {"slider_to_cart": 0.0} + damping = {"slider_to_cart": 0.0} ctrl_frequency = 250 desired_sim_frequency = 500 @@ -119,25 +120,12 @@ class termination_weight: termination = 0.0 class algorithm(FixedRobotCfgPPO.algorithm): - # training params - value_loss_coef = 1.0 - use_clipped_value_loss = True - clip_param = 0.2 - entropy_coef = 0.01 - num_learning_epochs = 5 - # * mini batch size = num_envs*nsteps / nminibatches - num_mini_batches = 4 - learning_rate = 1.0e-3 - schedule = "adaptive" # could be adaptive, fixed - discount_horizon = 1.0 # [s] - GAE_bootstrap_horizon = 0.2 # [s] - desired_kl = 0.01 - max_grad_norm = 1.0 + pass class runner(FixedRobotCfgPPO.runner): policy_class_name = "ActorCritic" algorithm_class_name = "PPO2" - num_steps_per_env = 96 # per iteration + num_steps_per_env = 32 # per iteration max_iterations = 500 # number of policy updates # * logging diff --git a/gym/envs/mini_cheetah/mini_cheetah_config.py b/gym/envs/mini_cheetah/mini_cheetah_config.py index 157c6031..ba2655f7 100644 --- a/gym/envs/mini_cheetah/mini_cheetah_config.py +++ b/gym/envs/mini_cheetah/mini_cheetah_config.py @@ -138,7 +138,7 @@ class actor(LeggedRobotRunnerCfg.actor): "dof_vel", "dof_pos_target", ] - normalize_obs = True + normalize_obs = False actions = ["dof_pos_target"] add_noise = True disable_actions = False @@ -167,7 +167,6 @@ class critic(LeggedRobotRunnerCfg.critic): "dof_vel", "dof_pos_target", ] - normalize_obs = True class reward: class weights: diff --git a/gym/envs/mini_cheetah/mini_cheetah_osc_config.py b/gym/envs/mini_cheetah/mini_cheetah_osc_config.py index 9d686f8e..b16bf919 100644 --- a/gym/envs/mini_cheetah/mini_cheetah_osc_config.py +++ b/gym/envs/mini_cheetah/mini_cheetah_osc_config.py @@ -69,27 +69,28 @@ class control(MiniCheetahCfg.control): class osc: process_noise_std = 0.25 + grf_threshold = 0.1 # 20. # Normalized to body weight # oscillator parameters - omega = 3 # 0.5 #3.5 # in Hz - coupling = 1 # 0.02 - osc_bool = False - grf_bool = False + omega = 3 # gets overwritten + coupling = 1 # gets overwritten + osc_bool = False # not used in paper + grf_bool = False # not used in paper randomize_osc_params = False - grf_threshold = 0.1 # 20. # Normalized to body weight + # randomization ranges: not used in paper omega_range = [1.0, 4.0] # [0.0, 10.] coupling_range = [ 0.0, 1.0, ] # with normalized grf, can have omega/coupling on same scale offset_range = [0.0, 0.0] + + # choice of oscillator parameters, see paper equation (5) stop_threshold = 0.5 omega_stop = 1.0 omega_step = 2.0 omega_slope = 1.0 omega_max = 4.0 omega_var = 0.25 - # coupling_step = 0. - # coupling_stop = 0. coupling_stop = 4.0 coupling_step = 1.0 coupling_slope = 0.0 @@ -160,11 +161,10 @@ class scaling(MiniCheetahCfg.scaling): class MiniCheetahOscRunnerCfg(MiniCheetahRunnerCfg): seed = -1 - runner_class_name = "OnPolicyRunner" + runner_class_name = "MyRunner" - class policy: + class actor(MiniCheetahRunnerCfg.actor): hidden_dims = [256, 256, 128] - critic_hidden_dims = [256, 256, 128] # * can be elu, relu, selu, crelu, lrelu, tanh, sigmoid activation = "elu" smooth_exploration = False @@ -178,8 +178,25 @@ class policy: "oscillator_obs", "dof_pos_target", ] + actions = ["dof_pos_target"] + normalize_obs = False - critic_obs = [ + class noise(MiniCheetahRunnerCfg.actor.noise): + pass + + class critic(MiniCheetahRunnerCfg.critic): + hidden_dims = [128, 128] + activation = "relu" + normalize_obs = False + + critic_class_name = "Critic" # "DenseSpectralLatent" + # * some class-specific params + # minimize = False + # relative_dim = 8 # 16 + # latent_dim = 32 # 18, + # latent_hidden_dims = [128, 128] + # latent_activation = "relu" + obs = [ "base_height", "base_lin_vel", "base_ang_vel", @@ -188,14 +205,9 @@ class policy: "dof_pos_obs", "dof_vel", "oscillator_obs", - "oscillators_vel", "dof_pos_target", ] - - actions = ["dof_pos_target"] - - class noise: - pass + normalize_obs = False class reward: class weights: @@ -227,20 +239,23 @@ class termination_weight: termination = 15.0 / 100.0 class algorithm(MiniCheetahRunnerCfg.algorithm): - # training params - value_loss_coef = 1.0 - use_clipped_value_loss = True + # both + gamma = 0.99 + lam = 0.98 + # new + storage_size = 2**17 + batch_size = 2**15 + max_grad_steps = 24 + clip_param = 0.2 - entropy_coef = 0.02 - num_learning_epochs = 4 - # mini batch size = num_envs*nsteps/nminibatches - num_mini_batches = 8 learning_rate = 1.0e-5 - schedule = "adaptive" # can be adaptive, fixed - discount_horizon = 1.0 - GAE_bootstrap_horizon = 2.0 - desired_kl = 0.01 max_grad_norm = 1.0 + # Critic + use_clipped_value_loss = True + # Actor + entropy_coef = 0.01 + schedule = "adaptive" # could be adaptive, fixed + desired_kl = 0.01 class runner(MiniCheetahRunnerCfg.runner): run_name = "" diff --git a/gym/envs/mini_cheetah/mini_cheetah_ref_config.py b/gym/envs/mini_cheetah/mini_cheetah_ref_config.py index 74e60d59..e9af92ac 100644 --- a/gym/envs/mini_cheetah/mini_cheetah_ref_config.py +++ b/gym/envs/mini_cheetah/mini_cheetah_ref_config.py @@ -72,9 +72,9 @@ class MiniCheetahRefRunnerCfg(MiniCheetahRunnerCfg): class actor(MiniCheetahRunnerCfg.actor): hidden_dims = [256, 256, 128] # * can be elu, relu, selu, crelu, lrelu, tanh, sigmoid - activation = "elu" smooth_exploration = False exploration_sample_freq = 16 + activation = ["elu", "elu", "tanh"] obs = [ "base_ang_vel", "projected_gravity", @@ -83,7 +83,7 @@ class actor(MiniCheetahRunnerCfg.actor): "dof_vel", "phase_obs", ] - normalize_obs = True + normalize_obs = False actions = ["dof_pos_target"] disable_actions = False @@ -98,10 +98,10 @@ class noise: ang_vel = [0.3, 0.15, 0.4] gravity_vec = 0.1 - class critic(MiniCheetahRunnerCfg.critic): - hidden_dims = [256, 256, 128] + class critic: + hidden_dims = [256, 128] # * can be elu, relu, selu, crelu, lrelu, tanh, sigmoid - activation = "elu" + activation = ["elu", "tanh"] obs = [ "base_height", "base_lin_vel", @@ -113,7 +113,7 @@ class critic(MiniCheetahRunnerCfg.critic): "phase_obs", "dof_pos_target", ] - normalize_obs = True + normalize_obs = False class reward: class weights: diff --git a/gym/envs/pendulum/pendulum.py b/gym/envs/pendulum/pendulum.py index 722b88d1..a3bbc355 100644 --- a/gym/envs/pendulum/pendulum.py +++ b/gym/envs/pendulum/pendulum.py @@ -1,3 +1,4 @@ +from math import sqrt import torch import numpy as np @@ -19,6 +20,28 @@ def _reset_system(self, env_ids): [self.dof_pos[env_ids].sin(), self.dof_pos[env_ids].cos()], dim=1 ) + def _check_terminations_and_timeouts(self): + super()._check_terminations_and_timeouts() + self.terminated = self.timed_out + + def reset_to_uniform(self, env_ids): + grid_points = int(sqrt(self.num_envs)) + lin_pos = torch.linspace( + self.dof_pos_range[0, 0], + self.dof_pos_range[0, 1], + grid_points, + device=self.device, + ) + lin_vel = torch.linspace( + self.dof_vel_range[0, 0], + self.dof_vel_range[0, 1], + grid_points, + device=self.device, + ) + grid = torch.cartesian_prod(lin_pos, lin_vel) + self.dof_pos[env_ids] = grid[:, 0].unsqueeze(-1) + self.dof_vel[env_ids] = grid[:, 1].unsqueeze(-1) + def _reward_theta(self): theta_rwd = torch.cos(self.dof_pos[:, 0]) # no scaling return self._sqrdexp(theta_rwd) @@ -28,16 +51,22 @@ def _reward_omega(self): return self._sqrdexp(omega_rwd) def _reward_equilibrium(self): - theta_norm = self._normalize_theta() - omega = self.dof_vel[:, 0] - error = torch.stack( - [theta_norm / self.scales["dof_pos"], omega / self.scales["dof_vel"]], dim=1 - ) - return self._sqrdexp(torch.mean(error, dim=1), scale=0.01) + # theta_norm = self._normalize_theta() + # omega = self.dof_vel[:, 0] + # error = torch.stack( + # [theta_norm / self.scales["dof_pos"], omega / self.scales["dof_vel"]], dim=1 + # ) + # return self._sqrdexp(torch.mean(error, dim=1), 0.01) + + # todo compare alternatives + error = torch.abs(self.dof_state) + error[:, 0] /= self.scales["dof_pos"] + error[:, 1] /= self.scales["dof_vel"] + return self._sqrdexp(torch.mean(error, dim=1), sigma=0.01) def _reward_torques(self): """Penalize torques""" - return self._sqrdexp(torch.mean(torch.square(self.torques), dim=1), scale=0.2) + return self._sqrdexp(torch.mean(torch.square(self.torques), dim=1), sigma=0.2) def _reward_energy(self): kinetic_energy = ( diff --git a/gym/envs/pendulum/pendulum_config.py b/gym/envs/pendulum/pendulum_config.py index 561087f6..bb33c1f1 100644 --- a/gym/envs/pendulum/pendulum_config.py +++ b/gym/envs/pendulum/pendulum_config.py @@ -33,7 +33,7 @@ class init_state(FixedRobotCfg.init_state): class control(FixedRobotCfg.control): actuated_joints_mask = [1] # angle - ctrl_frequency = 100 + ctrl_frequency = 25 desired_sim_frequency = 200 stiffness = {"theta": 0.0} # [N*m/rad] damping = {"theta": 0.0} # [N*m*s/rad] diff --git a/gym/utils/helpers.py b/gym/utils/helpers.py index 90af8bce..1e421dac 100644 --- a/gym/utils/helpers.py +++ b/gym/utils/helpers.py @@ -191,7 +191,7 @@ def get_args(custom_parameters=None): { "name": "--task", "type": str, - "default": "cartpole", + "default": "pendulum", "help": "Resume training or start testing from a checkpoint. " "Overrides config file if provided.", }, diff --git a/learning/__init__.py b/learning/__init__.py index 34f15bd6..15c19314 100644 --- a/learning/__init__.py +++ b/learning/__init__.py @@ -29,3 +29,9 @@ # THE POSSIBILITY OF SUCH DAMAGE. # # Copyright (c) 2021 ETH Zurich, Nikita Rudin + +import os + +LEGGED_GYM_ROOT_DIR = os.path.dirname( + os.path.dirname(os.path.realpath(__file__)) +) diff --git a/learning/algorithms/__init__.py b/learning/algorithms/__init__.py index 78231181..7211a720 100644 --- a/learning/algorithms/__init__.py +++ b/learning/algorithms/__init__.py @@ -33,4 +33,4 @@ from .ppo import PPO from .ppo2 import PPO2 from .SE import StateEstimator -from .sac import SAC \ No newline at end of file +from .sac import SAC diff --git a/learning/modules/QRCritics.py b/learning/modules/QRCritics.py new file mode 100644 index 00000000..a65ec304 --- /dev/null +++ b/learning/modules/QRCritics.py @@ -0,0 +1,849 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F + +from learning.modules.utils import RunningMeanStd, create_MLP + + +def create_lower_diagonal(x, n, device): + L = torch.zeros((*x.shape[:-1], n, n), device=device, requires_grad=False) + tril_indices = torch.tril_indices(row=n, col=n, offset=0) + rows, cols = tril_indices + L[..., rows, cols] = x + return L + + +def create_PD_lower_diagonal(x, n, device): + tril_indices = torch.tril_indices(row=n, col=n, offset=0) + diag_indices = (tril_indices[0] == tril_indices[1]).nonzero(as_tuple=True)[0] + x[..., diag_indices] = F.softplus(x[..., diag_indices]) + L = torch.zeros((*x.shape[:-1], n, n), device=device, requires_grad=False) + rows, cols = tril_indices + L[..., rows, cols] = x + return L + + +def compose_cholesky(L): + return torch.einsum("...ij, ...jk -> ...ik", L, L.transpose(-2, -1)) + + +def quadratify_xAx(x, A): + return torch.einsum( + "...ij, ...jk -> ...ik", + torch.einsum("...ij, ...jk -> ...ik", x.unsqueeze(-1).transpose(-2, -1), A), + x.unsqueeze(-1), + ).squeeze() + + +def init_weights(m): + if isinstance(m, nn.Linear): + torch.nn.init.xavier_uniform_(m.weight) + m.bias.data.fill_(0.01) + + +def least_squares_fit(x, y): + x_flat = x.view(-1, x.shape[-1]) + y_flat = y.view(-1, y.shape[-1]) + ones = torch.ones(x_flat.shape[0], 1, device=x.device) + x_aug = torch.cat([ones, x_flat], dim=1) + # Use torch.linalg.lstsq to find the least-squares solution + result = torch.linalg.lstsq(x_aug, y_flat) + coefficients = result.solution + bias = coefficients[0] + weights = coefficients[1:] + + return weights, bias + + +def forward_affine(x, weights, bias): + x_flat = x.view(-1, x.shape[-1]) + x_aug = torch.cat( + [torch.ones(x_flat.shape[0], 1, device=x_flat.device), x_flat], dim=1 + ) + return x_aug.matmul(torch.cat([bias.unsqueeze(0), weights], dim=0)) + + +class Critic(nn.Module): + def __init__( + self, + num_obs, + hidden_dims=None, + activation="elu", + normalize_obs=True, + device="cuda", + **kwargs, + ): + super().__init__() + + self.NN = create_MLP(num_obs, 1, hidden_dims, activation) + self._normalize_obs = normalize_obs + if self._normalize_obs: + self.obs_rms = RunningMeanStd(num_obs) + self.value_offset = nn.Parameter(torch.zeros(1, device=device)) + + def forward(self, x, return_all=False): + if self._normalize_obs: + with torch.no_grad(): + x = self.obs_rms(x) + value = self.NN(x).squeeze() + self.value_offset + if return_all: + return {"value": value} + else: + return value + + def evaluate(self, critic_observations, return_all=False): + return self.forward(critic_observations, return_all=return_all) + + def loss_fn(self, obs, target, **kwargs): + return nn.functional.mse_loss(self.forward(obs), target, reduction="mean") + + +class OuterProduct(nn.Module): + def __init__( + self, + num_obs, + hidden_dims, + latent_dim=None, + activation="elu", + dropouts=None, + normalize_obs=False, + minimize=False, + device="cuda", + **kwargs, + ): + super().__init__() + self.device = device + if latent_dim is None: + latent_dim = num_obs + self.latent_dim = latent_dim + self.minimize = minimize + if minimize: + self.sign = torch.ones(1, device=device) + else: + self.sign = -torch.ones(1, device=device) + self.value_offset = nn.Parameter(torch.zeros(1, device=device)) + + self._normalize_obs = normalize_obs + if self._normalize_obs: + self.obs_rms = RunningMeanStd(num_obs) + + self.NN = create_MLP(num_obs, latent_dim, hidden_dims, activation, dropouts) + # todo: have a linear NN to automatically coord-shift input + + def forward(self, x, return_all=False): + z = self.NN(x) + # outer product. Both of these are equivalent + A = z.unsqueeze(-1) @ z.unsqueeze(-2) + # A2 = torch.einsum("nmx,nmy->nmxy", z, z) + # value = self.sign * quadratify_xAx(x, A) + value = quadratify_xAx(x, A) + value *= 1.0 if self.minimize else -1.0 + value += self.value_offset + + if return_all: + # return value, A, L + return {"value": value, "A": A} + else: + return value + + def evaluate(self, obs, return_all=False): + return self.forward(obs, return_all) + + def loss_fn(self, obs, target, **kwargs): + loss_NN = F.mse_loss(self.forward(obs), target, reduction="mean") + + return loss_NN + + +class OuterProductLatent(OuterProduct): + def __init__( + self, + num_obs, + hidden_dims, + latent_dim=None, + activation="elu", + dropouts=None, + normalize_obs=False, + minimize=False, + latent_hidden_dims=[128, 128], + latent_activation="tanh", + device="cuda", + **kwargs, + ): + super().__init__( + num_obs, + hidden_dims, + latent_dim, + activation, + dropouts, + normalize_obs, + minimize, + device, + **kwargs, + ) + self.latent_NN = create_MLP( + num_obs, latent_dim, latent_hidden_dims, latent_activation + ) + + def forward(self, x, return_all=False): + z = self.NN(x) + # outer product. Both of these are equivalent + A = z.unsqueeze(-1) @ z.unsqueeze(-2) + # A2 = torch.einsum("nmx,nmy->nmxy", z, z) + value = self.sign * quadratify_xAx(self.latent_NN(x), A) + value += self.value_offset + + if return_all: + # return value, A, L + return {"value": value, "A": A, "z": z} + else: + return value + + +class CholeskyInput(nn.Module): + def __init__( + self, + num_obs, + hidden_dims, + latent_dim=None, + activation="elu", + dropouts=None, + normalize_obs=False, + minimize=False, + device="cuda", + **kwargs, + ): + super().__init__() + self.device = device + if latent_dim is None: + latent_dim = num_obs + self.latent_dim = latent_dim + self.minimize = minimize + if minimize: + self.sign = torch.ones(1, device=device) + else: + self.sign = -torch.ones(1, device=device) + self.value_offset = nn.Parameter(torch.zeros(1, device=device)) + # V = x'Ax + b = a* x'Ax + b, and add a regularization for det(A) ~= 1 + # self.scaling_quadratic = nn.Parameter(torch.ones(1, device=device)) + + self._normalize_obs = normalize_obs + if self._normalize_obs: + self.obs_rms = RunningMeanStd(num_obs) + + num_lower_diag_elements = sum(range(latent_dim + 1)) + self.lower_diag_NN = create_MLP( + num_obs, num_lower_diag_elements, hidden_dims, activation, dropouts + ) + # self.lower_diag_NN.apply(init_weights) + + def forward(self, x, return_all=False): + output = self.lower_diag_NN(x) + L = create_lower_diagonal(output, self.latent_dim, self.device) + A = compose_cholesky(L) + value = self.sign * quadratify_xAx(x, A) + value += self.value_offset + # value *= self.scaling_quadratic + + if return_all: + # return value, A, L + return {"value": value, "A": A, "L": L} + else: + return value + # return self.sign * quadratify_xAx(x, A) + self.value_offset + + def evaluate(self, obs, return_all=False): + return self.forward(obs, return_all) + + def loss_fn(self, obs, target, **kwargs): + return F.mse_loss(self.forward(obs), target, reduction="mean") + + +class CholeskyLatent(CholeskyInput): + def __init__( + self, + num_obs, + hidden_dims, + latent_dim, + latent_hidden_dims=[64], + latent_activation="tanh", + latent_dropouts=None, + activation="elu", + dropouts=None, + normalize_obs=False, + minimize=False, + device="cuda", + **kwargs, + ): + super().__init__( + num_obs, + hidden_dims, + latent_dim=latent_dim, + activation=activation, + dropouts=dropouts, + normalize_obs=normalize_obs, + minimize=minimize, + device="cuda", + **kwargs, + ) + + self.latent_NN = create_MLP( + num_obs, latent_dim, latent_hidden_dims, latent_activation + ) + + # self.latent_NN.apply(init_weights) + + def forward(self, x, return_all=False): + z = self.latent_NN(x) + output = self.lower_diag_NN(x) + L = create_lower_diagonal(output, self.latent_dim, self.device) + A = compose_cholesky(L) + + value = self.sign * quadratify_xAx(z, A) + # do not affect value offset in this part of the loss + value += self.value_offset + # value *= self.scaling_quadratic + + if return_all: + # return value, A, L, z + return {"value": value, "A": A, "L": L, "z": z} + else: + return value + + def evaluate(self, obs, return_all=False): + return self.forward(obs, return_all) + + +class PDCholeskyInput(CholeskyInput): + def forward(self, x, return_all=False): + output = self.lower_diag_NN(x) + L = create_PD_lower_diagonal(output, self.latent_dim, self.device) + A = compose_cholesky(L) + # assert (torch.linalg.eigvals(A).real > 0).all() + # value = self.sign * quadratify_xAx(x, A) + value = quadratify_xAx(x, A) + value *= 1.0 if self.minimize else -1.0 + value += self.value_offset + # value *= self.scaling_quadratic + + if return_all: + # return value, A, L + return {"value": value, "A": A, "L": L} + else: + return value + # return self.sign * quadratify_xAx(x, A) + self.value_offset + + +class PDCholeskyLatent(CholeskyLatent): + def forward(self, x, return_all=False): + z = self.latent_NN(x) + output = self.lower_diag_NN(x) + L = create_PD_lower_diagonal(output, self.latent_dim, self.device) + A = compose_cholesky(L) + # assert (torch.linalg.eigvals(A).real > 0).all() + value = self.sign * quadratify_xAx(z, A) + value += self.value_offset + # value *= self.scaling_quadratic + + if return_all: + # return value, A, L + return {"value": value, "A": A, "L": L} + else: + return value + + +class SpectralLatent(nn.Module): + def __init__( + self, + num_obs, + hidden_dims, + activation="elu", + dropouts=None, + normalize_obs=False, + relative_dim=None, + latent_dim=None, + latent_hidden_dims=[64], + latent_activation="tanh", + latent_dropouts=None, + minimize=False, + device="cuda", + **kwargs, + ): + super().__init__() + self.device = device + if relative_dim is None: + relative_dim = num_obs + if latent_dim is None: + latent_dim = num_obs + + self.num_obs = num_obs + self.relative_dim = relative_dim + self.latent_dim = latent_dim + self.minimize = minimize + if minimize: + self.sign = torch.ones(1, device=device) + else: + self.sign = -torch.ones(1, device=device) + self.value_offset = nn.Parameter(torch.zeros(1, device=device)) + # self.scaling_quadratic = nn.Parameter(torch.ones(1, device=device)) + + self._normalize_obs = normalize_obs + if self._normalize_obs: + self.obs_rms = RunningMeanStd(num_obs) + # TODO up to here, same as Cholesky, can be refactored + n_outputs = ( + torch.tril_indices(self.relative_dim, self.latent_dim).shape[1] + + relative_dim + ) + self.spectral_NN = create_MLP( + num_obs, n_outputs, hidden_dims, activation, dropouts + ) + self.latent_NN = create_MLP( + num_obs, latent_dim, latent_hidden_dims, latent_activation + ) + + def forward(self, x, return_all=False): + z = self.latent_NN(x) + y = self.spectral_NN(x) + A_diag = torch.diag_embed(F.softplus(y[..., : self.relative_dim])) + # tril_indices = torch.tril_indices(self.latent_dim, self.relative_dim) + tril_indices = torch.tril_indices(self.relative_dim, self.latent_dim) + L = torch.zeros( + (*x.shape[:-1], self.relative_dim, self.latent_dim), device=self.device + ) + L[..., tril_indices[0], tril_indices[1]] = y[..., self.relative_dim :] + # Compute (L^T A_diag) L + A = torch.einsum( + "...ij,...jk->...ik", + torch.einsum("...ik,...kj->...ij", L.transpose(-1, -2), A_diag), + L, + ) + # assert (torch.linalg.eigvals(A).real >= -1e-6).all() + # ! This fails, but so far with just -e-10, so really really small... + value = self.sign * quadratify_xAx(z, A) + # with torch.no_grad(): + value += self.value_offset + + if return_all: + # return value, z, torch.diag(A_diag), L, A + return { + "value": value, + "z": z, + # "A_diag": torch.diag(A_diag), #! throws shape error + "L": L, + "A": A, + } + else: + return value + + def evaluate(self, obs, return_all=False): + return self.forward(obs, return_all) + + def loss_fn(self, obs, target, **kwargs): + loss_NN = F.mse_loss(self.forward(obs), target, reduction="mean") + + # if self.minimize: + # loss_offset = (self.value_offset / target.min() - 1.0).pow(2) + # else: + # loss_offset = (self.value_offset / target.max() - 1.0).pow(2) + + # loss_scaling = (self.scaling_quadratic - target.mean()).pow(2) + + return loss_NN # + loss_offset # + loss_scaling + + +class DenseSpectralLatent(nn.Module): + def __init__( + self, + num_obs, + hidden_dims, + activation="elu", + dropouts=None, + normalize_obs=False, + relative_dim=None, + latent_dim=None, + latent_hidden_dims=[64], + latent_activation="tanh", + latent_dropouts=None, + minimize=False, + device="cuda", + **kwargs, + ): + super().__init__() + self.device = device + if relative_dim is None: + relative_dim = num_obs + if latent_dim is None: + latent_dim = num_obs + + self.num_obs = num_obs + self.relative_dim = relative_dim + self.latent_dim = latent_dim + self.minimize = minimize + if minimize: + self.sign = torch.ones(1, device=device) + else: + self.sign = -torch.ones(1, device=device) + self.value_offset = nn.Parameter(torch.zeros(1, device=device)) + # self.scaling_quadratic = nn.Parameter(torch.ones(1, device=device)) + + self._normalize_obs = normalize_obs + if self._normalize_obs: + self.obs_rms = RunningMeanStd(num_obs) + # TODO up to here, same as Cholesky, can be refactored + n_zeros = sum(range(relative_dim)) + n_outputs = relative_dim + relative_dim * latent_dim - n_zeros + self.rel_fill = n_zeros + # n_outputs = ( + # relative_dim * latent_dim + # - torch.tril_indices(self.relative_dim, self.latent_dim).shape[1] + # + relative_dim + # ) + self.spectral_NN = create_MLP( + num_obs, n_outputs, hidden_dims, activation, dropouts + ) + self.latent_NN = create_MLP( + num_obs, latent_dim, latent_hidden_dims, latent_activation + ) + + def forward(self, x, return_all=False): + z = self.latent_NN(x) + y = self.spectral_NN(x) + A_diag = torch.diag_embed(F.softplus(y[..., : self.relative_dim])) + # tril_indices = torch.tril_indices(self.latent_dim, self.relative_dim) + tril_indices = torch.tril_indices(self.relative_dim, self.latent_dim) + L = torch.zeros( + (*x.shape[:-1], self.relative_dim, self.latent_dim), device=self.device + ) + L[..., tril_indices[0], tril_indices[1]] = y[ + ..., self.relative_dim : self.relative_dim + tril_indices.shape[1] + ] + L[..., :, self.relative_dim :] = y[ + ..., self.relative_dim + tril_indices.shape[1] : + ].view(L[..., :, self.relative_dim :].shape) + + # Compute (L^T A_diag) L + A = torch.einsum( + "...ij,...jk->...ik", + torch.einsum("...ik,...kj->...ij", L.transpose(-1, -2), A_diag), + L, + ) + # A = torch.einsum( + # "...ij,...jk->...ik", + # torch.einsum("...ik,...kj->...ij", L, A_diag), + # L.transpose(-1, -2), + # ) + # assert (torch.linalg.eigvals(A).real >= -1e-6).all() + # ! This fails, but so far with just -e-10, so really really small... + value = self.sign * quadratify_xAx(z, A) + # with torch.no_grad(): + value += self.value_offset + + if return_all: + # return value, z, torch.diag(A_diag), L, A + return { + "value": value, + "z": z, + # "A_diag": torch.diag(A_diag), #! throws shape error + "L": L, + "A": A, + } + else: + return value + + def evaluate(self, obs, return_all=False): + return self.forward(obs, return_all) + + def loss_fn(self, obs, target, **kwargs): + loss_NN = F.mse_loss(self.forward(obs), target, reduction="mean") + + # if self.minimize: + # loss_offset = (self.value_offset / target.min() - 1.0).pow(2) + # else: + # loss_offset = (self.value_offset / target.max() - 1.0).pow(2) + + # loss_scaling = (self.scaling_quadratic - target.mean()).pow(2) + + return loss_NN # + loss_offset # + loss_scaling + + +class QR(nn.Module): + def __init__( + self, + num_obs, + hidden_dims, + action_dim, + latent_dim=None, + activation="elu", + dropouts=None, + normalize_obs=False, + minimize=False, + device="cuda", + **kwargs, + ): + super().__init__() + self.device = device + if latent_dim is None: + latent_dim = num_obs + self.latent_dim = latent_dim + self.action_dim = action_dim + self.minimize = minimize + if minimize: + self.sign = torch.ones(1, device=device) + else: + self.sign = -torch.ones(1, device=device) + + self._normalize_obs = normalize_obs + if self._normalize_obs: + self.obs_rms = RunningMeanStd(num_obs) + + # Make Q estimator + num_lower_diag_elements_Q = sum(range(self.latent_dim + 1)) + self.lower_diag_NN_Q = create_MLP( + latent_dim, num_lower_diag_elements_Q, hidden_dims, activation, dropouts + ) + # self.lower_diag_NN_Q.apply(init_weights) + + # Make R estimator + num_lower_diag_elements_R = sum(range(action_dim + 1)) + self.lower_diag_NN_R = create_MLP( + latent_dim, num_lower_diag_elements_R, hidden_dims, activation, dropouts + ) + # self.lower_diag_NN_R.apply(init_weights) + + def forward(self, z, u, return_all=False): + Q_lower_diag = self.lower_diag_NN_Q(z) + L_Q = create_lower_diagonal(Q_lower_diag, self.latent_dim, self.device) + Q = compose_cholesky(L_Q) + + R_lower_diag = self.lower_diag_NN_R(z) + L_R = create_lower_diagonal(R_lower_diag, self.action_dim, self.device) + R = compose_cholesky(L_R) + + if return_all: + # return Q, L_Q, R, L_R + return {"Q": Q, "L_Q": L_Q, "R": R, "L_R": L_R} + else: + return Q, R + + def evaluate(self, obs, return_all=False): + return self.forward(obs, return_all) + + def loss_fn(self, z, target, actions, **kwargs): + Q, R = self.forward(z, target) + Q_value = self.sign * quadratify_xAx(z, Q) + R_value = self.sign * quadratify_xAx(actions, R) + gae_loss = F.mse_loss(Q_value + R_value, target, reduction="mean") + return gae_loss + + def riccati_loss_fn(self, z, target, P, **kwargs): + Q, R = self.forward(z, target) + A, B = self.linearize_pendulum_dynamics() + A = A.to(self.device) + B = B.to(self.device) + AT_P_A = torch.einsum( + "...ij, ...ik -> ...ik", + torch.einsum("...ij, ...jk -> ...ik", A.transpose(-1, -2), P), + A, + ) + AT_P_B = torch.einsum( + "...ij, ...ik -> ...ik", + torch.einsum("...ij, ...jk -> ...ik", A.transpose(-1, -2), P), + B, + ) + R_BT_B = R + torch.einsum("...ij, ...jk -> ...ik", B.transpose(-1, -2), B) + BT_P_A = torch.einsum( + "...ij, ...ik -> ...ik", + torch.einsum("...ij, ...jk -> ...ik", B.transpose(-1, -2), P), + A, + ) + final_term = torch.einsum( + "...ij, ...jk -> ...ik", + torch.einsum("...ij, ...jk -> ...ik", AT_P_B, torch.linalg.inv(R_BT_B)), + BT_P_A, + ) + riccati_loss = Q + AT_P_A - P - final_term + return F.mse_loss(riccati_loss, target=0.0, reduction="mean") + + def linearize_pendulum_dynamics(self, x_desired=torch.tensor([0.0, 0.0])): + m = 1.0 + b = 0.1 + length = 1.0 + g = 9.81 + ml2 = m * length**2 + + A = torch.tensor( + [[0.0, 1.0], [g / length * torch.cos(x_desired[0]), -b / ml2]], + device=self.device, + ) + B = torch.tensor([[0.0], [(1.0 / ml2)]], device=self.device) + return A, B + + +class QPNet(nn.Module): + def __init__( + self, + num_obs, + hidden_dims, + latent_dim=None, + activation="elu", + dropouts=None, + normalize_obs=False, + minimize=False, + device="cuda", + **kwargs, + ): + super().__init__() + self.device = device + if latent_dim is None: + latent_dim = num_obs + self.latent_dim = latent_dim + self.minimize = minimize + if minimize: + self.sign = torch.ones(1, device=device) + else: + self.sign = -torch.ones(1, device=device) + self.value_offset = nn.Parameter(torch.zeros(1, device=device)) + # V = x'Ax + b = a* x'Ax + b, and add a regularization for det(A) ~= 1 + self.scaling_quadratic = nn.Parameter(torch.ones(1, device=device)) + + self._normalize_obs = normalize_obs + if self._normalize_obs: + self.obs_rms = RunningMeanStd(num_obs) + + self.num_lower_diag_elements = sum(range(latent_dim + 1)) + self.lower_diag_NN = create_MLP( + num_obs, + self.num_lower_diag_elements + num_obs, + hidden_dims, + activation, + dropouts, + ) # ! num_obs added to output to create c for c.T@x + # self.lower_diag_NN.apply(init_weights) + + def forward(self, x, return_all=False): + res = self.lower_diag_NN(x) + output = res[..., : self.num_lower_diag_elements] + c = res[..., self.num_lower_diag_elements :] + L = create_lower_diagonal(output, self.latent_dim, self.device) + A = compose_cholesky(nn.ReLU()(L)) + value = self.sign * quadratify_xAx(x, A) + torch.einsum( + "...ij, ...ij -> ...i", c, x + ) + value += self.value_offset + # value *= self.scaling_quadratic + + if return_all: + # return value, A, L + return {"value": value, "A": A, "L": L, "c": c} + else: + return value + + def evaluate(self, obs, return_all=False): + return self.forward(obs, return_all) + + def loss_fn(self, obs, target, **kwargs): + loss_NN = F.mse_loss(self.forward(obs), target, reduction="mean") + + # loss_scaling = (self.scaling_quadratic - target.mean()).pow(2) + + return loss_NN # + loss_offset + loss_scaling + + +class NN_wRiccati(nn.Module): + def __init__(self, **kwargs): + super().__init__() + # assert ( + # "critic_name" in kwargs.keys() + # ), "Name of custom critic not specified in NN with Riccati init" + # assert "action_dim" in kwargs.keys(), "Dimension of NN actions not specified." + critic_name = kwargs["critic_name"] + device = kwargs["device"] + self.has_latent = False if kwargs["latent_dim"] is None else True + self.critic = eval(f"{critic_name}(**kwargs).to(device)") + self.QR_network = QR(**kwargs).to(device) + + def forward(self, x, return_all=False): + return self.critic.forward(x, return_all) + + def evaluate(self, obs, return_all=False): + return self.forward(obs, return_all) + + def loss_fn(self, obs, target, actions, **kwargs): + return self.value_loss(obs, target, actions) + self.reg_loss( + obs, target, actions + ) + + def value_loss(self, obs, target, actions, **kwargs): + return self.critic.loss_fn(obs, target, actions=actions) + + def reg_loss(self, obs, target, actions, **kwargs): + with torch.no_grad(): + P = self.critic(obs, return_all=True)["A"] + z = obs if self.has_latent is False else self.critic.latent_NN(obs) + return self.QR_network.riccati_loss_fn(z, target, P=P) + + +class NN_wQR(nn.Module): + def __init__(self, **kwargs): + super().__init__() + # assert ( + # "critic_name" in kwargs.keys() + # ), "Name of custom critic not specified in NN with Riccati init" + # assert "action_dim" in kwargs.keys(), "Dimension of NN actions not specified." + critic_name = kwargs["critic_name"] + device = kwargs["device"] + self.has_latent = False if kwargs["latent_dim"] is None else True + self.critic = eval(f"{critic_name}(**kwargs).to(device)") + self.QR_network = QR(**kwargs).to(device) + + def forward(self, x, return_all=False): + return self.critic.forward(x, return_all) + + def evaluate(self, obs, return_all=False): + return self.forward(obs, return_all) + + def loss_fn(self, obs, target, actions, **kwargs): + return self.value_loss(obs, target, actions) + self.reg_loss( + obs, target, actions + ) + + def value_loss(self, obs, target, actions, **kwargs): + return self.critic.loss_fn(obs, target, actions=actions) + + def reg_loss(self, obs, target, actions, **kwargs): + z = obs if self.has_latent is False else self.critic.latent_NN(obs) + return self.QR_network.loss_fn(z, target, actions=actions) + + +class NN_wLinearLatent(nn.Module): + def __init__(self, **kwargs): + super().__init__() + critic_name = kwargs["critic_name"] + device = kwargs["device"] # noqa + self.critic = eval(f"{critic_name}(**kwargs).to(device)") + + @property + def value_offset(self): + return self.critic.value_offset + + def forward(self, x, return_all=False): + return self.critic.forward(x, return_all) + + def evaluate(self, obs, return_all=False): + return self.forward(obs, return_all) + + def loss_fn(self, obs, target, actions, **kwargs): + return self.value_loss(obs, target, actions) + self.reg_loss( + obs, target, actions + ) + + def value_loss(self, obs, target, actions, **kwargs): + return self.critic.loss_fn(obs, target, actions=actions) + + def reg_loss(self, obs, target, actions, **kwargs): + z = self.critic.latent_NN(obs) + u = actions.detach().clone() + u = u.view(-1, u.shape[-1]) + K, z_offset = least_squares_fit(z, u) + return F.mse_loss(forward_affine(z, K, z_offset), target=u, reduction="mean") diff --git a/learning/modules/__init__.py b/learning/modules/__init__.py index f608cb29..9505377d 100644 --- a/learning/modules/__init__.py +++ b/learning/modules/__init__.py @@ -36,3 +36,4 @@ from .critic import Critic from .chimera_actor import ChimeraActor from .smooth_actor import SmoothActor +from .QRCritics import * diff --git a/learning/modules/critic.py b/learning/modules/critic.py index 96c53438..f7960bfa 100644 --- a/learning/modules/critic.py +++ b/learning/modules/critic.py @@ -1,5 +1,6 @@ import torch import torch.nn as nn + from .utils import create_MLP from .utils import RunningMeanStd @@ -29,5 +30,5 @@ def forward(self, x): def evaluate(self, critic_observations): return self.forward(critic_observations) - def loss_fn(self, obs, target): + def loss_fn(self, obs, target, **kwargs): return nn.functional.mse_loss(self.forward(obs), target, reduction="mean") diff --git a/learning/modules/utils/neural_net.py b/learning/modules/utils/neural_net.py index b18efe96..af782a53 100644 --- a/learning/modules/utils/neural_net.py +++ b/learning/modules/utils/neural_net.py @@ -1,32 +1,46 @@ import torch import os import copy -import numpy as np -def create_MLP(num_inputs, num_outputs, hidden_dims, activation, dropouts=None): - activation = get_activation(activation) - +def create_MLP( + num_inputs, num_outputs, hidden_dims, activations, dropouts=None, just_list=False +): + if not isinstance(activations, list): + activations = [activations] * len(hidden_dims) if dropouts is None: dropouts = [0] * len(hidden_dims) - + elif not isinstance(dropouts, list): + dropouts = [dropouts] * len(hidden_dims) layers = [] - if not hidden_dims: # handle no hidden layers + # first layer + if len(hidden_dims) > 0: + add_layer(layers, num_inputs, hidden_dims[0], activations[0], dropouts[0]) + for i in range(len(hidden_dims) - 1): + add_layer( + layers, + hidden_dims[i], + hidden_dims[i + 1], + activations[i + 1], + dropouts[i + 1], + ) + else: + add_layer(layers, hidden_dims[-1], num_outputs) + else: # handle no hidden dims, just linear layer add_layer(layers, num_inputs, num_outputs) + + if just_list: + return layers else: - add_layer(layers, num_inputs, hidden_dims[0], activation, dropouts[0]) - for i in range(len(hidden_dims)): - if i == len(hidden_dims) - 1: - add_layer(layers, hidden_dims[i], num_outputs) - else: - add_layer( - layers, - hidden_dims[i], - hidden_dims[i + 1], - activation, - dropouts[i + 1], - ) - return torch.nn.Sequential(*layers) + return torch.nn.Sequential(*layers) + + +def add_layer(layer_list, num_inputs, num_outputs, activation=None, dropout=0): + layer_list.append(torch.nn.Linear(num_inputs, num_outputs)) + if dropout > 0: + layer_list.append(torch.nn.Dropout(p=dropout)) + if activation is not None: + layer_list.append(get_activation(activation)) def get_activation(act_name): @@ -37,27 +51,29 @@ def get_activation(act_name): elif act_name == "relu": return torch.nn.ReLU() elif act_name == "crelu": - return torch.nn.ReLU() + return torch.nn.CELU() elif act_name == "lrelu": return torch.nn.LeakyReLU() elif act_name == "tanh": return torch.nn.Tanh() elif act_name == "sigmoid": return torch.nn.Sigmoid() + elif act_name == "softplus": + return torch.nn.Softplus() + elif act_name == "softmax": + return torch.nn.Softmax(dim=-1) + elif act_name == "randrelu": + return torch.nn.RReLU() + elif act_name == "softsign": + return torch.nn.Softsign() + elif act_name == "mish": + return torch.nn.Mish() else: print("invalid activation function!") return None -def add_layer(layer_list, num_inputs, num_outputs, activation=None, dropout=0): - layer_list.append(torch.nn.Linear(num_inputs, num_outputs)) - if dropout > 0: - layer_list.append(torch.nn.Dropout(p=dropout)) - if activation is not None: - layer_list.append(activation) - - -def export_network(network, network_name, path, num_inputs, latent=False): +def export_network(network, network_name, path, num_inputs): """ Thsi function traces and exports the given network module in .pt and .onnx file formats. These can be used for evaluation on other systems @@ -73,24 +89,11 @@ def export_network(network, network_name, path, num_inputs, latent=False): path_TS = os.path.join(path, network_name + ".pt") # TorchScript path path_onnx = os.path.join(path, network_name + ".onnx") # ONNX path model = copy.deepcopy(network).to("cpu") + model.device = "cpu" # force all tensors created as intermediate steps to CPU # To trace model, must be evaluated once with arbitrary input model.eval() dummy_input = torch.rand((num_inputs)) + # dummy_input = torch.rand((2, num_inputs)) model_traced = torch.jit.trace(model, dummy_input) torch.jit.save(model_traced, path_TS) torch.onnx.export(model_traced, dummy_input, path_onnx) - - if latent: - # Export latent model - path_latent = os.path.join(path, network_name + "_latent.onnx") - model_latent = torch.nn.Sequential(model.obs_rms, model.latent_net) - model_latent.eval() - dummy_input = torch.rand((num_inputs)) - model_traced = torch.jit.trace(model_latent, dummy_input) - torch.onnx.export(model_traced, dummy_input, path_latent) - - # Save actor std of shape (num_actions, latent_dim) - # It is important that the shape is the same as the exploration matrix - path_std = os.path.join(path, network_name + "_std.txt") - std_transposed = model.get_std.numpy().T - np.savetxt(path_std, std_transposed) diff --git a/learning/runners/BaseRunner.py b/learning/runners/BaseRunner.py index 6819f4f4..01a1b825 100644 --- a/learning/runners/BaseRunner.py +++ b/learning/runners/BaseRunner.py @@ -36,6 +36,9 @@ def parse_train_cfg(self, train_cfg): self.cfg = train_cfg["runner"] self.alg_cfg = train_cfg["algorithm"] remove_zero_weighted_rewards(train_cfg["critic"]["reward"]["weights"]) + remove_zero_weighted_rewards( + train_cfg["critic"]["reward"]["termination_weight"] + ) self.actor_cfg = train_cfg["actor"] self.critic_cfg = train_cfg["critic"] diff --git a/learning/runners/__init__.py b/learning/runners/__init__.py index a0840b06..6735a86d 100644 --- a/learning/runners/__init__.py +++ b/learning/runners/__init__.py @@ -33,4 +33,6 @@ from .on_policy_runner import OnPolicyRunner from .my_runner import MyRunner from .old_policy_runner import OldPolicyRunner -from .off_policy_runner import OffPolicyRunner \ No newline at end of file +from .off_policy_runner import OffPolicyRunner +from .custom_critic_runner import CustomCriticRunner +from .datalogging_runner import DataLoggingRunner diff --git a/learning/runners/custom_critic_runner.py b/learning/runners/custom_critic_runner.py new file mode 100644 index 00000000..fbfb8c05 --- /dev/null +++ b/learning/runners/custom_critic_runner.py @@ -0,0 +1,178 @@ +import os +import numpy as np +import torch +from tensordict import TensorDict + +from gym import LEGGED_GYM_ROOT_DIR +from learning.algorithms import * # noqa: F403 +from learning.modules import Actor +from learning.utils import Logger + +from .on_policy_runner import OnPolicyRunner +from learning.storage import DictStorage + +logger = Logger() +storage = DictStorage() + + +class CustomCriticRunner(OnPolicyRunner): + def __init__(self, env, train_cfg, device="cpu"): + super().__init__(env, train_cfg, device) + logger.initialize( + self.env.num_envs, + self.env.dt, + self.cfg["max_iterations"], + self.device, + ) + + def _set_up_alg(self): + num_actor_obs = self.get_obs_size(self.actor_cfg["obs"]) + num_actions = self.get_action_size(self.actor_cfg["actions"]) + num_critic_obs = self.get_obs_size(self.critic_cfg["obs"]) # noqa: F841 + critic_class_name = self.critic_cfg["critic_class_name"] + actor = Actor(num_actor_obs, num_actions, **self.actor_cfg) + critic = eval(f"{critic_class_name}(num_critic_obs, **self.critic_cfg)") + alg_class = eval(self.cfg["algorithm_class_name"]) + self.alg = alg_class(actor, critic, device=self.device, **self.alg_cfg) + + def learn(self): + self.set_up_logger() + + rewards_dict = {} + + self.alg.switch_to_train() + actor_obs = self.get_obs(self.actor_cfg["obs"]) + critic_obs = self.get_obs(self.critic_cfg["obs"]) + tot_iter = self.it + self.num_learning_iterations + self.all_obs = torch.zeros(self.env.num_envs * (tot_iter - self.it + 1), 2) + self.all_obs[: self.env.num_envs, :] = actor_obs + self.save() + + # * start up storage + transition = TensorDict({}, batch_size=self.env.num_envs, device=self.device) + transition.update( + { + "actor_obs": actor_obs, + "actions": self.alg.act(actor_obs, critic_obs), + "critic_obs": critic_obs, + "rewards": self.get_rewards({"termination": 0.0})["termination"], + "dones": self.get_timed_out(), + } + ) + storage.initialize( + transition, + self.env.num_envs, + self.env.num_envs * self.num_steps_per_env, + device=self.device, + ) + + logger.tic("runtime") + for self.it in range(self.it + 1, tot_iter + 1): + logger.tic("iteration") + logger.tic("collection") + # * Rollout + with torch.inference_mode(): + for i in range(self.num_steps_per_env): + actions = self.alg.act(actor_obs, critic_obs) + self.set_actions( + self.actor_cfg["actions"], + actions, + self.actor_cfg["disable_actions"], + ) + + transition.update( + { + "actor_obs": actor_obs, + "actions": actions, + "critic_obs": critic_obs, + } + ) + + self.env.step() + + actor_obs = self.get_noisy_obs( + self.actor_cfg["obs"], self.actor_cfg["noise"] + ) + critic_obs = self.get_obs(self.critic_cfg["obs"]) + start = self.env.num_envs * self.it + end = self.env.num_envs * (self.it + 1) + self.all_obs[start:end, :] = actor_obs + + # * get time_outs + timed_out = self.get_timed_out() + terminated = self.get_terminated() + dones = timed_out | terminated + + self.update_rewards(rewards_dict, terminated) + total_rewards = torch.stack(tuple(rewards_dict.values())).sum(dim=0) + + transition.update( + { + "rewards": total_rewards, + "timed_out": timed_out, + "dones": dones, + } + ) + storage.add_transitions(transition) + + logger.log_rewards(rewards_dict) + logger.log_rewards({"total_rewards": total_rewards}) + logger.finish_step(dones) + logger.toc("collection") + + logger.tic("learning") + self.alg.update(storage.data) + storage.clear() + logger.toc("learning") + logger.log_all_categories() + + logger.finish_iteration() + logger.toc("iteration") + logger.toc("runtime") + logger.print_to_terminal() + + if self.it % self.save_interval == 0: + self.save() + self.all_obs = self.all_obs.detach().cpu().numpy() + save_path = os.path.join( + LEGGED_GYM_ROOT_DIR, + "logs", + "lqrc", + "standard_training_data.npy" + if "Cholesky" not in self.critic_cfg["critic_class_name"] + else "custom_training_data.npy", + ) + + dir_path = os.path.dirname(save_path) + if not os.path.exists(dir_path): + os.makedirs(dir_path) + np.save(save_path, self.all_obs) + print(f"Saved training observations to {save_path}") + self.save() + + def update_rewards(self, rewards_dict, terminated): + rewards_dict.update( + self.get_rewards( + self.critic_cfg["reward"]["termination_weight"], mask=terminated + ) + ) + rewards_dict.update( + self.get_rewards( + self.critic_cfg["reward"]["weights"], + modifier=self.env.dt, + mask=~terminated, + ) + ) + + def set_up_logger(self): + logger.register_rewards(list(self.critic_cfg["reward"]["weights"].keys())) + logger.register_rewards( + list(self.critic_cfg["reward"]["termination_weight"].keys()) + ) + logger.register_rewards(["total_rewards"]) + logger.register_category( + "algorithm", self.alg, ["mean_value_loss", "mean_surrogate_loss"] + ) + logger.register_category("actor", self.alg.actor, ["action_std", "entropy"]) + + logger.attach_torch_obj_to_wandb((self.alg.actor, self.alg.critic)) diff --git a/learning/runners/datalogging_runner.py b/learning/runners/datalogging_runner.py new file mode 100644 index 00000000..7a227d78 --- /dev/null +++ b/learning/runners/datalogging_runner.py @@ -0,0 +1,213 @@ +import os +import torch +from tensordict import TensorDict + +from learning.utils import Logger + +from .BaseRunner import BaseRunner +from learning.storage import DictStorage +from learning.utils import export_to_numpy + +logger = Logger() +storage = DictStorage() + + +class DataLoggingRunner(BaseRunner): + def __init__(self, env, train_cfg, device="cpu"): + super().__init__(env, train_cfg, device) + logger.initialize( + self.env.num_envs, + self.env.dt, + self.cfg["max_iterations"], + self.device, + ) + + def learn(self): + self.set_up_logger() + + rewards_dict = {} + + self.alg.switch_to_train() + actor_obs = self.get_obs(self.actor_cfg["obs"]) + critic_obs = self.get_obs(self.critic_cfg["obs"]) + tot_iter = self.it + self.num_learning_iterations + + # * start up storage + transition = TensorDict({}, batch_size=self.env.num_envs, device=self.device) + transition.update( + { + "actor_obs": actor_obs, + "next_actor_obs": actor_obs, + "actions": self.alg.act(actor_obs), + "critic_obs": critic_obs, + "next_critic_obs": critic_obs, + "rewards": self.get_rewards({"termination": 0.0})["termination"], + "timed_out": self.get_timed_out(), + "terminated": self.get_terminated(), + "dones": self.get_timed_out() | self.get_terminated(), + } + ) + storage.initialize( + transition, + self.env.num_envs, + self.env.num_envs * self.num_steps_per_env, + device=self.device, + ) + + self.save() + # burn in observation normalization. + if self.actor_cfg["normalize_obs"] or self.critic_cfg["normalize_obs"]: + self.burn_in_normalization() + self.env.reset() + + logger.tic("runtime") + for self.it in range(self.it + 1, tot_iter + 1): + logger.tic("iteration") + logger.tic("collection") + # * Rollout + with torch.inference_mode(): + for i in range(self.num_steps_per_env): + actions = self.alg.act(actor_obs) + self.set_actions( + self.actor_cfg["actions"], + actions, + self.actor_cfg["disable_actions"], + ) + + transition.update( + { + "actor_obs": actor_obs, + "actions": actions, + "critic_obs": critic_obs, + } + ) + + self.env.step() + + actor_obs = self.get_noisy_obs( + self.actor_cfg["obs"], self.actor_cfg["noise"] + ) + critic_obs = self.get_obs(self.critic_cfg["obs"]) + + # * get time_outs + timed_out = self.get_timed_out() + terminated = self.get_terminated() + dones = timed_out | terminated + + self.update_rewards(rewards_dict, terminated) + total_rewards = torch.stack(tuple(rewards_dict.values())).sum(dim=0) + + transition.update( + { + "next_actor_obs": actor_obs, + "next_critic_obs": critic_obs, + "rewards": total_rewards, + "timed_out": timed_out, + "terminated": terminated, + "dones": dones, + } + ) + storage.add_transitions(transition) + + logger.log_rewards(rewards_dict) + logger.log_rewards({"total_rewards": total_rewards}) + logger.finish_step(dones) + logger.toc("collection") + + logger.tic("learning") + self.save() + self.alg.update(storage.data) + storage.clear() + logger.toc("learning") + logger.log_all_categories() + + logger.finish_iteration() + logger.toc("iteration") + logger.toc("runtime") + logger.print_to_terminal() + + # if self.it % self.save_interval == 0: + # self.save() + # self.save() + # print(f"Saved data from LQR Pendulum run to {dict_save_path}") + + @torch.no_grad + def burn_in_normalization(self, n_iterations=100): + actor_obs = self.get_obs(self.actor_cfg["obs"]) + critic_obs = self.get_obs(self.critic_cfg["obs"]) + for _ in range(n_iterations): + actions = self.alg.act(actor_obs) + self.set_actions(self.actor_cfg["actions"], actions) + self.env.step() + actor_obs = self.get_noisy_obs( + self.actor_cfg["obs"], self.actor_cfg["noise"] + ) + critic_obs = self.get_obs(self.critic_cfg["obs"]) + self.alg.critic.evaluate(critic_obs) + self.env.reset() + + def update_rewards(self, rewards_dict, terminated): + rewards_dict.update( + self.get_rewards( + self.critic_cfg["reward"]["termination_weight"], mask=terminated + ) + ) + rewards_dict.update( + self.get_rewards( + self.critic_cfg["reward"]["weights"], + modifier=self.env.dt, + mask=~terminated, + ) + ) + + def set_up_logger(self): + logger.register_rewards(list(self.critic_cfg["reward"]["weights"].keys())) + logger.register_rewards( + list(self.critic_cfg["reward"]["termination_weight"].keys()) + ) + logger.register_rewards(["total_rewards"]) + logger.register_category( + "algorithm", self.alg, ["mean_value_loss", "mean_surrogate_loss"] + ) + logger.register_category("actor", self.alg.actor, ["action_std", "entropy"]) + + logger.attach_torch_obj_to_wandb((self.alg.actor, self.alg.critic)) + + def save(self): + os.makedirs(self.log_dir, exist_ok=True) + path = os.path.join(self.log_dir, "model_{}.pt".format(self.it)) + torch.save( + { + "actor_state_dict": self.alg.actor.state_dict(), + "critic_state_dict": self.alg.critic.state_dict(), + "optimizer_state_dict": self.alg.optimizer.state_dict(), + "critic_optimizer_state_dict": self.alg.critic_optimizer.state_dict(), + "iter": self.it, + }, + path, + ) + path_data = os.path.join(self.log_dir, "data_{}".format(self.it)) + torch.save(storage.data.cpu(), path_data + ".pt") + export_to_numpy(storage.data, path_data + ".npz") + + def load(self, path, load_optimizer=True): + loaded_dict = torch.load(path) + self.alg.actor.load_state_dict(loaded_dict["actor_state_dict"]) + self.alg.critic.load_state_dict(loaded_dict["critic_state_dict"]) + if load_optimizer: + self.alg.optimizer.load_state_dict(loaded_dict["optimizer_state_dict"]) + self.alg.critic_optimizer.load_state_dict( + loaded_dict["critic_optimizer_state_dict"] + ) + self.it = loaded_dict["iter"] + + def switch_to_eval(self): + self.alg.actor.eval() + self.alg.critic.eval() + + def get_inference_actions(self): + obs = self.get_noisy_obs(self.actor_cfg["obs"], self.actor_cfg["noise"]) + return self.alg.actor.act_inference(obs) + + def export(self, path): + self.alg.actor.export(path) diff --git a/learning/runners/my_runner.py b/learning/runners/my_runner.py index d97dee8d..b5317e2e 100644 --- a/learning/runners/my_runner.py +++ b/learning/runners/my_runner.py @@ -4,6 +4,10 @@ from learning.utils import Logger from .on_policy_runner import OnPolicyRunner from learning.storage import DictStorage +from learning.algorithms import PPO2 # noqa F401 +from learning.modules.actor import Actor +from learning.modules.critic import Critic # noqa F401 +from learning.modules.QRCritics import * # noqa F401 logger = Logger() storage = DictStorage() @@ -19,14 +23,24 @@ def __init__(self, env, train_cfg, device="cpu"): self.device, ) + def _set_up_alg(self): + num_actor_obs = self.get_obs_size(self.actor_cfg["obs"]) + num_actions = self.get_action_size(self.actor_cfg["actions"]) + num_critic_obs = self.get_obs_size(self.critic_cfg["obs"]) # noqa: F841 + actor = Actor(num_actor_obs, num_actions, **self.actor_cfg) + critic_class_name = self.critic_cfg["critic_class_name"] + critic = eval(f"{critic_class_name}(num_critic_obs, **self.critic_cfg)") + alg_class = eval(self.cfg["algorithm_class_name"]) + self.alg = alg_class(actor, critic, device=self.device, **self.alg_cfg) + def learn(self): self.set_up_logger() rewards_dict = {} self.alg.switch_to_train() - actor_obs = self.get_obs(self.actor_cfg["actor_obs"]) - critic_obs = self.get_obs(self.critic_cfg["critic_obs"]) + actor_obs = self.get_obs(self.actor_cfg["obs"]) + critic_obs = self.get_obs(self.critic_cfg["obs"]) tot_iter = self.it + self.num_learning_iterations self.save() @@ -35,10 +49,14 @@ def learn(self): transition.update( { "actor_obs": actor_obs, - "actions": self.alg.act(actor_obs, critic_obs), + "next_actor_obs": actor_obs, + "actions": self.alg.act(actor_obs), "critic_obs": critic_obs, + "next_critic_obs": critic_obs, "rewards": self.get_rewards({"termination": 0.0})["termination"], - "dones": self.get_timed_out(), + "timed_out": self.get_timed_out(), + "terminated": self.get_terminated(), + "dones": self.get_timed_out() | self.get_terminated(), } ) storage.initialize( @@ -48,6 +66,9 @@ def learn(self): device=self.device, ) + # burn in observation normalization. + self.burn_in_normalization() + logger.tic("runtime") for self.it in range(self.it + 1, tot_iter + 1): logger.tic("iteration") @@ -55,7 +76,7 @@ def learn(self): # * Rollout with torch.inference_mode(): for i in range(self.num_steps_per_env): - actions = self.alg.act(actor_obs, critic_obs) + actions = self.alg.act(actor_obs) self.set_actions( self.actor_cfg["actions"], actions, @@ -73,9 +94,9 @@ def learn(self): self.env.step() actor_obs = self.get_noisy_obs( - self.actor_cfg["actor_obs"], self.actor_cfg["noise"] + self.actor_cfg["obs"], self.actor_cfg["noise"] ) - critic_obs = self.get_obs(self.critic_cfg["critic_obs"]) + critic_obs = self.get_obs(self.critic_cfg["obs"]) # * get time_outs timed_out = self.get_timed_out() terminated = self.get_terminated() @@ -86,8 +107,11 @@ def learn(self): transition.update( { + "next_actor_obs": actor_obs, + "next_critic_obs": critic_obs, "rewards": total_rewards, "timed_out": timed_out, + "terminated": terminated, "dones": dones, } ) @@ -125,3 +149,20 @@ def set_up_logger(self): logger.register_category("actor", self.alg.actor, ["action_std", "entropy"]) logger.attach_torch_obj_to_wandb((self.alg.actor, self.alg.critic)) + + @torch.no_grad + def burn_in_normalization(self, n_iterations=100): + actor_obs = self.get_obs(self.actor_cfg["obs"]) + critic_obs = self.get_obs(self.critic_cfg["obs"]) + for _ in range(n_iterations): + actions = self.alg.act(actor_obs) + self.set_actions(self.actor_cfg["actions"], actions) + self.env.step() + actor_obs = self.get_noisy_obs( + self.actor_cfg["obs"], self.actor_cfg["noise"] + ) + critic_obs = self.get_obs(self.critic_cfg["obs"]) + rewards = self.alg.critic.evaluate(critic_obs) + self.alg.critic.value_offset.copy_(rewards.mean()) + print(f"Value offset: {self.alg.critic.value_offset.item()}") + self.env.reset() diff --git a/learning/runners/on_policy_runner.py b/learning/runners/on_policy_runner.py index f601ce71..78b21225 100644 --- a/learning/runners/on_policy_runner.py +++ b/learning/runners/on_policy_runner.py @@ -42,7 +42,9 @@ def learn(self, states_to_log_dict=None): "critic_obs": critic_obs, "next_critic_obs": critic_obs, "rewards": self.get_rewards({"termination": 0.0})["termination"], - "dones": self.get_timed_out(), + "timed_out": self.get_timed_out(), + "terminated": self.get_terminated(), + "dones": self.get_timed_out() | self.get_terminated(), } ) storage.initialize( @@ -106,6 +108,7 @@ def learn(self, states_to_log_dict=None): "next_critic_obs": critic_obs, "rewards": total_rewards, "timed_out": timed_out, + "terminated": terminated, "dones": dones, } ) diff --git a/learning/utils/dict_utils.py b/learning/utils/dict_utils.py index 99534c22..73039526 100644 --- a/learning/utils/dict_utils.py +++ b/learning/utils/dict_utils.py @@ -1,21 +1,29 @@ +import numpy as np import torch from tensordict import TensorDict @torch.no_grad def compute_MC_returns(data: TensorDict, gamma, critic=None): + # todo not as accurate as taking if critic is None: last_values = torch.zeros_like(data["rewards"][0]) else: last_values = critic.evaluate(data["critic_obs"][-1]) returns = torch.zeros_like(data["rewards"]) - returns[-1] = last_values * ~data["dones"][-1] + returns[-1] = data["rewards"][-1] + gamma * last_values * ~data["terminated"][-1] for k in reversed(range(data["rewards"].shape[0] - 1)): not_done = ~data["dones"][k] returns[k] = data["rewards"][k] + gamma * returns[k + 1] * not_done - - return normalize(returns) + if critic is not None: + returns[k] += ( + gamma + * critic.evaluate(data["critic_obs"][k]) + * data["timed_out"][k] + * ~data["terminated"][k] + ) + return returns @torch.no_grad @@ -29,7 +37,6 @@ def compute_generalized_advantages(data, gamma, lam, critic): advantages = torch.zeros_like(data["values"]) if last_values is not None: # todo check this - # since we don't have observations for the last step, need last value plugged in not_done = ~data["dones"][-1] advantages[-1] = ( data["rewards"][-1] @@ -53,22 +60,16 @@ def compute_generalized_advantages(data, gamma, lam, critic): # todo change num_epochs to num_batches @torch.no_grad -def create_uniform_generator( - data, batch_size, num_epochs=1, max_gradient_steps=None, keys=None -): +def create_uniform_generator(data, batch_size, max_gradient_steps=100): n, m = data.shape total_data = n * m if batch_size > total_data: - Warning("Batch size is larger than total data, using available data only.") batch_size = total_data num_batches_per_epoch = total_data // batch_size - if max_gradient_steps: - if max_gradient_steps < num_batches_per_epoch: - num_batches_per_epoch = max_gradient_steps - num_epochs = max_gradient_steps // num_batches_per_epoch - num_epochs = max(num_epochs, 1) + num_epochs = max_gradient_steps // num_batches_per_epoch + last_epoch_batches = max_gradient_steps - num_epochs * num_batches_per_epoch for epoch in range(num_epochs): indices = torch.randperm(total_data, device=data.device) @@ -77,3 +78,19 @@ def create_uniform_generator( indices[i * batch_size : (i + 1) * batch_size] ] yield batched_data + else: + indices = torch.randperm(total_data, device=data.device) + for i in range(last_epoch_batches): + batched_data = data.flatten(0, 1)[ + indices[i * batch_size : (i + 1) * batch_size] + ] + yield batched_data + + +@torch.no_grad +def export_to_numpy(data, path): + # check if path ends iwth ".npz", and if not append it. + if not path.endswith(".npz"): + path += ".npz" + np.savez_compressed(path, **{key: val.cpu().numpy() for key, val in data.items()}) + return diff --git a/requirements.txt b/requirements.txt index 3f100110..923df0e8 100644 --- a/requirements.txt +++ b/requirements.txt @@ -13,4 +13,5 @@ ruff pre-commit onnx onnxruntime -tensordict \ No newline at end of file +tensordict +optuna \ No newline at end of file From 1540cd2d5db745052cdafbf1d2cdc61b975fd6d5 Mon Sep 17 00:00:00 2001 From: Steve Heim Date: Wed, 28 Aug 2024 11:33:10 -0400 Subject: [PATCH 16/77] fix faster training configs for benchmarking --- .../mini_cheetah/mini_cheetah_osc_config.py | 4 +-- .../mini_cheetah/mini_cheetah_ref_config.py | 32 +++++++++++++++---- 2 files changed, 28 insertions(+), 8 deletions(-) diff --git a/gym/envs/mini_cheetah/mini_cheetah_osc_config.py b/gym/envs/mini_cheetah/mini_cheetah_osc_config.py index b16bf919..e244f54d 100644 --- a/gym/envs/mini_cheetah/mini_cheetah_osc_config.py +++ b/gym/envs/mini_cheetah/mini_cheetah_osc_config.py @@ -166,7 +166,7 @@ class MiniCheetahOscRunnerCfg(MiniCheetahRunnerCfg): class actor(MiniCheetahRunnerCfg.actor): hidden_dims = [256, 256, 128] # * can be elu, relu, selu, crelu, lrelu, tanh, sigmoid - activation = "elu" + activation = ["elu", "elu", "tanh"] smooth_exploration = False obs = [ @@ -186,7 +186,7 @@ class noise(MiniCheetahRunnerCfg.actor.noise): class critic(MiniCheetahRunnerCfg.critic): hidden_dims = [128, 128] - activation = "relu" + activation = ["relu", "tanh"] normalize_obs = False critic_class_name = "Critic" # "DenseSpectralLatent" diff --git a/gym/envs/mini_cheetah/mini_cheetah_ref_config.py b/gym/envs/mini_cheetah/mini_cheetah_ref_config.py index e9af92ac..69b4ee60 100644 --- a/gym/envs/mini_cheetah/mini_cheetah_ref_config.py +++ b/gym/envs/mini_cheetah/mini_cheetah_ref_config.py @@ -25,9 +25,9 @@ class control(MiniCheetahCfg.control): # * PD Drive parameters: stiffness = {"haa": 20.0, "hfe": 20.0, "kfe": 20.0} damping = {"haa": 0.5, "hfe": 0.5, "kfe": 0.5} - gait_freq = 3.0 + gait_freq = 2.5 ctrl_frequency = 100 - desired_sim_frequency = 500 + desired_sim_frequency = 300 class commands(MiniCheetahCfg.commands): pass @@ -132,7 +132,7 @@ class weights: dof_pos_limits = 0.0 feet_contact_forces = 0.0 dof_near_home = 0.0 - reference_traj = 1.5 + reference_traj = 0.0 swing_grf = 1.5 stance_grf = 1.5 @@ -140,11 +140,31 @@ class termination_weight: termination = 0.15 class algorithm(MiniCheetahRunnerCfg.algorithm): - pass + # both + gamma = 0.99 + lam = 0.95 + # shared + batch_size = 2**15 + max_gradient_steps = 36 + # new + storage_size = 2**17 # new + batch_size = 2**15 # new + + clip_param = 0.2 + learning_rate = 1.0e-3 + max_grad_norm = 1.0 + # Critic + use_clipped_value_loss = True + # Actor + entropy_coef = 0.01 + schedule = "adaptive" # could be adaptive, fixed + desired_kl = 0.01 + lr_range = [2e-5, 1e-2] + lr_ratio = 1.5 class runner(MiniCheetahRunnerCfg.runner): run_name = "" experiment_name = "mini_cheetah_ref" - max_iterations = 1000 # number of policy updates + max_iterations = 500 # number of policy updates algorithm_class_name = "PPO2" - num_steps_per_env = 32 # deprecate + num_steps_per_env = 20 # deprecate From 842b39e9647a6b9455587cc77a230964d17c55b6 Mon Sep 17 00:00:00 2001 From: Steve Heim Date: Thu, 29 Aug 2024 11:57:35 -0400 Subject: [PATCH 17/77] just tweaks --- .../mini_cheetah/mini_cheetah_ref_config.py | 17 +++++++++++++---- learning/runners/my_runner.py | 9 +++------ scripts/train.py | 1 + 3 files changed, 17 insertions(+), 10 deletions(-) diff --git a/gym/envs/mini_cheetah/mini_cheetah_ref_config.py b/gym/envs/mini_cheetah/mini_cheetah_ref_config.py index 69b4ee60..5d2a1939 100644 --- a/gym/envs/mini_cheetah/mini_cheetah_ref_config.py +++ b/gym/envs/mini_cheetah/mini_cheetah_ref_config.py @@ -67,13 +67,13 @@ class scaling(MiniCheetahCfg.scaling): class MiniCheetahRefRunnerCfg(MiniCheetahRunnerCfg): seed = -1 - runner_class_name = "OnPolicyRunner" + runner_class_name = "MyRunner" class actor(MiniCheetahRunnerCfg.actor): hidden_dims = [256, 256, 128] # * can be elu, relu, selu, crelu, lrelu, tanh, sigmoid smooth_exploration = False - exploration_sample_freq = 16 + # exploration_sample_freq = 16 activation = ["elu", "elu", "tanh"] obs = [ "base_ang_vel", @@ -82,6 +82,7 @@ class actor(MiniCheetahRunnerCfg.actor): "dof_pos_obs", "dof_vel", "phase_obs", + "dof_pos_target", ] normalize_obs = False @@ -114,6 +115,14 @@ class critic: "dof_pos_target", ] normalize_obs = False + # critic_class_name = "Critic" # "DenseSpectralLatent" + critic_class_name = "DenseSpectralLatent" + # * some class-specific params + minimize = False + relative_dim = 4 # 16 + latent_dim = 12 # 18, + latent_hidden_dims = [256, 256] + latent_activation = "elu" class reward: class weights: @@ -142,7 +151,7 @@ class termination_weight: class algorithm(MiniCheetahRunnerCfg.algorithm): # both gamma = 0.99 - lam = 0.95 + lam = 0.75 # shared batch_size = 2**15 max_gradient_steps = 36 @@ -151,7 +160,7 @@ class algorithm(MiniCheetahRunnerCfg.algorithm): batch_size = 2**15 # new clip_param = 0.2 - learning_rate = 1.0e-3 + learning_rate = 1.0e-2 max_grad_norm = 1.0 # Critic use_clipped_value_loss = True diff --git a/learning/runners/my_runner.py b/learning/runners/my_runner.py index b5317e2e..17e0817b 100644 --- a/learning/runners/my_runner.py +++ b/learning/runners/my_runner.py @@ -144,16 +144,16 @@ def set_up_logger(self): ) logger.register_rewards(["total_rewards"]) logger.register_category( - "algorithm", self.alg, ["mean_value_loss", "mean_surrogate_loss"] + "algorithm", + self.alg, + ["mean_value_loss", "mean_surrogate_loss", "learning_rate"], ) logger.register_category("actor", self.alg.actor, ["action_std", "entropy"]) - logger.attach_torch_obj_to_wandb((self.alg.actor, self.alg.critic)) @torch.no_grad def burn_in_normalization(self, n_iterations=100): actor_obs = self.get_obs(self.actor_cfg["obs"]) - critic_obs = self.get_obs(self.critic_cfg["obs"]) for _ in range(n_iterations): actions = self.alg.act(actor_obs) self.set_actions(self.actor_cfg["actions"], actions) @@ -161,8 +161,5 @@ def burn_in_normalization(self, n_iterations=100): actor_obs = self.get_noisy_obs( self.actor_cfg["obs"], self.actor_cfg["noise"] ) - critic_obs = self.get_obs(self.critic_cfg["obs"]) - rewards = self.alg.critic.evaluate(critic_obs) - self.alg.critic.value_offset.copy_(rewards.mean()) print(f"Value offset: {self.alg.critic.value_offset.item()}") self.env.reset() diff --git a/scripts/train.py b/scripts/train.py index 759992cf..a2535408 100644 --- a/scripts/train.py +++ b/scripts/train.py @@ -31,3 +31,4 @@ def train(train_cfg, policy_runner): if __name__ == "__main__": train_cfg, policy_runner = setup() train(train_cfg=train_cfg, policy_runner=policy_runner) + print(f"Final value offset: {policy_runner.alg.critic.value_offset.item()}") From 33efaf5eda267147556449e970f7c1319ef3923f Mon Sep 17 00:00:00 2001 From: Steve Heim Date: Thu, 29 Aug 2024 13:07:29 -0400 Subject: [PATCH 18/77] SAC: typo and params that work for pendulum --- gym/envs/pendulum/pendulum_SAC_config.py | 10 +++++----- learning/modules/chimera_actor.py | 6 +++--- 2 files changed, 8 insertions(+), 8 deletions(-) diff --git a/gym/envs/pendulum/pendulum_SAC_config.py b/gym/envs/pendulum/pendulum_SAC_config.py index d555d5df..b094ee91 100644 --- a/gym/envs/pendulum/pendulum_SAC_config.py +++ b/gym/envs/pendulum/pendulum_SAC_config.py @@ -5,7 +5,7 @@ class PendulumSACCfg(PendulumCfg): class env(PendulumCfg.env): - num_envs = 1 + num_envs = 1024 episode_length_s = 10 class init_state(PendulumCfg.init_state): @@ -76,7 +76,7 @@ class reward: class weights: theta = 0.0 omega = 0.0 - equilibrium = 1.0 + equilibrium = 2.0 energy = 0.5 dof_vel = 0.0 torques = 0.025 @@ -85,8 +85,8 @@ class termination_weight: termination = 0.0 class algorithm(FixedRobotCfgPPO.algorithm): - initial_fill = 500 - storage_size = 10**6 # 17 + initial_fill = 0 + storage_size = 100 * 1024 # 10**6 # 17 batch_size = 256 # 4096 max_gradient_steps = 1 # 10 # SB3: 1 action_max = 2.0 @@ -106,7 +106,7 @@ class algorithm(FixedRobotCfgPPO.algorithm): class runner(FixedRobotCfgPPO.runner): run_name = "" experiment_name = "sac_pendulum" - max_iterations = 40_000 # number of policy updates + max_iterations = 30_000 # number of policy updates algorithm_class_name = "SAC" save_interval = 5000 num_steps_per_env = 1 diff --git a/learning/modules/chimera_actor.py b/learning/modules/chimera_actor.py index 1addc83d..a9ad77d6 100644 --- a/learning/modules/chimera_actor.py +++ b/learning/modules/chimera_actor.py @@ -36,19 +36,19 @@ def __init__( num_inputs=num_obs, num_outputs=hidden_dims["latent"][-1], hidden_dims=hidden_dims["latent"][:-1], - activation=activation["latent"], + activations=activation["latent"], ) self.mean_NN = create_MLP( num_inputs=hidden_dims["latent"][-1], num_outputs=num_actions, hidden_dims=hidden_dims["mean"], - activation=activation["mean"], + activations=activation["mean"], ) self.std_NN = create_MLP( num_inputs=hidden_dims["latent"][-1], num_outputs=num_actions, hidden_dims=hidden_dims["std"], - activation=activation["std"], + activations=activation["std"], ) # maybe zap From fd065559b0c3fa92a9bb71faf47d8db2950b3b64 Mon Sep 17 00:00:00 2001 From: Steve Heim Date: Fri, 30 Aug 2024 11:20:34 -0400 Subject: [PATCH 19/77] update ruff settings --- pyproject.toml | 2 -- 1 file changed, 2 deletions(-) diff --git a/pyproject.toml b/pyproject.toml index a1906be6..131f359f 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -40,8 +40,6 @@ target-version = "py38" # McCabe complexity (`C901`) by default. ignore = ["E722"] -ignore-init-module-imports = true - # Allow unused variables when underscore-prefixed. dummy-variable-rgx = "^(_+|(_+[a-zA-Z0-9_]*[a-zA-Z0-9]+?))$" From 3550dfd9b4b0a58b00d44587b655c6be7d1b2853 Mon Sep 17 00:00:00 2001 From: Steve Heim Date: Fri, 30 Aug 2024 11:22:10 -0400 Subject: [PATCH 20/77] address deprecation in torch.load and add lr to trackables for SAC --- learning/algorithms/sac.py | 16 +++++ learning/runners/__init__.py | 1 + learning/runners/datalogging_runner.py | 2 +- learning/runners/off_policy_runner.py | 6 +- learning/runners/old_policy_runner.py | 2 +- learning/runners/on_policy_runner.py | 2 +- learning/runners/psd_sac_runner.py | 58 +++++++++++++++++++ .../test_runner_integration.py | 2 +- 8 files changed, 84 insertions(+), 5 deletions(-) create mode 100644 learning/runners/psd_sac_runner.py diff --git a/learning/algorithms/sac.py b/learning/algorithms/sac.py index e0651d2d..7bda74cb 100644 --- a/learning/algorithms/sac.py +++ b/learning/algorithms/sac.py @@ -74,6 +74,22 @@ def __init__( def alpha(self): return self.log_alpha.exp() + @property + def alpha_lr(self): + return self.log_alpha_optimizer.param_groups[0]["lr"] + + @property + def critic_1_lr(self): + return self.critic_1_optimizer.param_groups[0]["lr"] + + @property + def critic_2_lr(self): + return self.critic_2_optimizer.param_groups[0]["lr"] + + @property + def actor_lr(self): + return self.actor_optimizer.param_groups[0]["lr"] + def switch_to_train(self): self.actor.train() self.critic_1.train() diff --git a/learning/runners/__init__.py b/learning/runners/__init__.py index 6735a86d..657b2f25 100644 --- a/learning/runners/__init__.py +++ b/learning/runners/__init__.py @@ -36,3 +36,4 @@ from .off_policy_runner import OffPolicyRunner from .custom_critic_runner import CustomCriticRunner from .datalogging_runner import DataLoggingRunner +from .psd_sac_runner import PSACRunner \ No newline at end of file diff --git a/learning/runners/datalogging_runner.py b/learning/runners/datalogging_runner.py index 7a227d78..2f853fae 100644 --- a/learning/runners/datalogging_runner.py +++ b/learning/runners/datalogging_runner.py @@ -191,7 +191,7 @@ def save(self): export_to_numpy(storage.data, path_data + ".npz") def load(self, path, load_optimizer=True): - loaded_dict = torch.load(path) + loaded_dict = torch.load(path, weights_only=True) self.alg.actor.load_state_dict(loaded_dict["actor_state_dict"]) self.alg.critic.load_state_dict(loaded_dict["critic_state_dict"]) if load_optimizer: diff --git a/learning/runners/off_policy_runner.py b/learning/runners/off_policy_runner.py index e57be9a9..7aed58e7 100644 --- a/learning/runners/off_policy_runner.py +++ b/learning/runners/off_policy_runner.py @@ -221,6 +221,10 @@ def set_up_logger(self): "mean_actor_loss", "mean_alpha_loss", "alpha", + "alpha_lr", + "critic_1_lr", + "critic_2_lr", + "actor_lr", ], ) # logger.register_category("actor", self.alg.actor, ["action_std", "entropy"]) @@ -246,7 +250,7 @@ def save(self): torch.save(save_dict, path) def load(self, path, load_optimizer=True): - loaded_dict = torch.load(path) + loaded_dict = torch.load(path, weights_only=True) self.alg.actor.load_state_dict(loaded_dict["actor_state_dict"]) self.alg.critic_1.load_state_dict(loaded_dict["critic_1_state_dict"]) self.alg.critic_2.load_state_dict(loaded_dict["critic_2_state_dict"]) diff --git a/learning/runners/old_policy_runner.py b/learning/runners/old_policy_runner.py index 42f019a3..d6fe5ddf 100644 --- a/learning/runners/old_policy_runner.py +++ b/learning/runners/old_policy_runner.py @@ -134,7 +134,7 @@ def save(self): ) def load(self, path, load_optimizer=True): - loaded_dict = torch.load(path) + loaded_dict = torch.load(path, weights_only=True) self.alg.actor_critic.actor.load_state_dict(loaded_dict["actor_state_dict"]) self.alg.actor_critic.critic.load_state_dict(loaded_dict["critic_state_dict"]) if load_optimizer: diff --git a/learning/runners/on_policy_runner.py b/learning/runners/on_policy_runner.py index 78b21225..0a81680d 100644 --- a/learning/runners/on_policy_runner.py +++ b/learning/runners/on_policy_runner.py @@ -193,7 +193,7 @@ def save(self): ) def load(self, path, load_optimizer=True): - loaded_dict = torch.load(path) + loaded_dict = torch.load(path, weights_only=True) self.alg.actor.load_state_dict(loaded_dict["actor_state_dict"]) self.alg.critic.load_state_dict(loaded_dict["critic_state_dict"]) if load_optimizer: diff --git a/learning/runners/psd_sac_runner.py b/learning/runners/psd_sac_runner.py new file mode 100644 index 00000000..0ba89104 --- /dev/null +++ b/learning/runners/psd_sac_runner.py @@ -0,0 +1,58 @@ +import os +import torch +from tensordict import TensorDict + +from learning.utils import Logger + +from learning.runners import OffPolicyRunner +from learning.modules import Critic, ChimeraActor +from learning.modules.QRCritics import * # noqa F401 + +from learning.storage import ReplayBuffer +from learning.algorithms import SAC + + +logger = Logger() +storage = ReplayBuffer() + + +class PSACRunner(OffPolicyRunner): + def __init__(self, env, train_cfg, device="cpu"): + super().__init__(env, train_cfg, device) + logger.initialize( + self.env.num_envs, + self.env.dt, + self.cfg["max_iterations"], + self.device, + ) + + def _set_up_alg(self): + num_actor_obs = self.get_obs_size(self.actor_cfg["obs"]) + num_actions = self.get_action_size(self.actor_cfg["actions"]) + num_critic_in = self.get_obs_size(self.critic_cfg["obs"]) + num_actions # noqa: F841 + actor = ChimeraActor(num_actor_obs, num_actions, **self.actor_cfg) + + critic_class = self.critic_cfg["critic_class_name"] + critic_1 = eval(f"{critic_class}(num_critic_in, **self.critic_cfg)") + critic_2 = eval(f"{critic_class}(num_critic_in, **self.critic_cfg)") + target_critic_1 = eval(f"{critic_class}(num_critic_in, **self.critic_cfg)") + target_critic_2 = eval(f"{critic_class}(num_critic_in, **self.critic_cfg)") + + # critic_1 = Critic(num_critic_obs + num_actions, **self.critic_cfg) + # critic_2 = Critic(num_critic_obs + num_actions, **self.critic_cfg) + + # target_critic_1 = Critic(num_critic_obs + num_actions, **self.critic_cfg) + # target_critic_2 = Critic(num_critic_obs + num_actions, **self.critic_cfg) + + print(actor) + print(critic_1) + + self.alg = SAC( + actor, + critic_1, + critic_2, + target_critic_1, + target_critic_2, + device=self.device, + **self.alg_cfg, + ) diff --git a/tests/integration_tests/test_runner_integration.py b/tests/integration_tests/test_runner_integration.py index 3310624f..ce645b5c 100644 --- a/tests/integration_tests/test_runner_integration.py +++ b/tests/integration_tests/test_runner_integration.py @@ -35,7 +35,7 @@ def load_saved_policy(runner): load_run=runner.cfg["load_run"], checkpoint=runner.cfg["checkpoint"], ) - loaded_dict = torch.load(resume_path) + loaded_dict = torch.load(resume_path, weights_only=True) actor.load_state_dict(loaded_dict["actor_state_dict"]) actor.eval() return actor From 4247ea7bf94d3c3cfe551020e3cc9cd27f16a490 Mon Sep 17 00:00:00 2001 From: Steve Heim Date: Fri, 30 Aug 2024 11:24:13 -0400 Subject: [PATCH 21/77] allow randomize_episode_counters for reset_to_uniform in pendulum --- gym/envs/pendulum/pendulum.py | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/gym/envs/pendulum/pendulum.py b/gym/envs/pendulum/pendulum.py index a3bbc355..90bc60ae 100644 --- a/gym/envs/pendulum/pendulum.py +++ b/gym/envs/pendulum/pendulum.py @@ -39,8 +39,8 @@ def reset_to_uniform(self, env_ids): device=self.device, ) grid = torch.cartesian_prod(lin_pos, lin_vel) - self.dof_pos[env_ids] = grid[:, 0].unsqueeze(-1) - self.dof_vel[env_ids] = grid[:, 1].unsqueeze(-1) + self.dof_pos[env_ids] = grid[env_ids, 0].unsqueeze(-1) + self.dof_vel[env_ids] = grid[env_ids, 1].unsqueeze(-1) def _reward_theta(self): theta_rwd = torch.cos(self.dof_pos[:, 0]) # no scaling @@ -54,8 +54,8 @@ def _reward_equilibrium(self): # theta_norm = self._normalize_theta() # omega = self.dof_vel[:, 0] # error = torch.stack( - # [theta_norm / self.scales["dof_pos"], omega / self.scales["dof_vel"]], dim=1 - # ) + # [theta_norm / self.scales["dof_pos"], omega / self.scales["dof_vel"]], + # dim=1) # return self._sqrdexp(torch.mean(error, dim=1), 0.01) # todo compare alternatives From 66c9e1b3b71c7067a737b88326ff249240772f8e Mon Sep 17 00:00:00 2001 From: Steve Heim Date: Tue, 3 Sep 2024 09:35:45 -0400 Subject: [PATCH 22/77] some slimmin and separation of utils --- gym/envs/__init__.py | 3 + gym/envs/pendulum/pendulum_PSD_config.py | 121 +++++++++++++++++++++++ learning/modules/QRCritics.py | 60 ++--------- learning/modules/__init__.py | 1 + learning/modules/utils/__init__.py | 1 + learning/modules/utils/matfncs.py | 53 ++++++++++ 6 files changed, 187 insertions(+), 52 deletions(-) create mode 100644 gym/envs/pendulum/pendulum_PSD_config.py create mode 100644 learning/modules/utils/matfncs.py diff --git a/gym/envs/__init__.py b/gym/envs/__init__.py index 1b78a261..0aa7ce31 100644 --- a/gym/envs/__init__.py +++ b/gym/envs/__init__.py @@ -34,6 +34,7 @@ "HumanoidRunningCfg": ".mit_humanoid.humanoid_running_config", "PendulumCfg": ".pendulum.pendulum_config", "PendulumSACCfg": ".pendulum.pendulum_SAC_config", + "PendulumPSDCfg": ".pendulum.pendulum_PSD_config", } runner_config_dict = { @@ -48,6 +49,7 @@ "HumanoidRunningRunnerCfg": ".mit_humanoid.humanoid_running_config", "PendulumRunnerCfg": ".pendulum.pendulum_config", "PendulumSACRunnerCfg": ".pendulum.pendulum_SAC_config", + "PendulumPSDRunnerCfg": ".pendulum.pendulum_PSD_config", } task_dict = { @@ -77,6 +79,7 @@ "flat_anymal_c": ["Anymal", "AnymalCFlatCfg", "AnymalCFlatRunnerCfg"], "pendulum": ["Pendulum", "PendulumCfg", "PendulumRunnerCfg"], "sac_pendulum": ["Pendulum", "PendulumSACCfg", "PendulumSACRunnerCfg"], + "psd_pendulum": ["Pendulum", "PendulumPSDCfg", "PendulumPSDRunnerCfg"], } for class_name, class_location in class_dict.items(): diff --git a/gym/envs/pendulum/pendulum_PSD_config.py b/gym/envs/pendulum/pendulum_PSD_config.py new file mode 100644 index 00000000..3de1ccca --- /dev/null +++ b/gym/envs/pendulum/pendulum_PSD_config.py @@ -0,0 +1,121 @@ +import torch +from gym.envs.base.fixed_robot_config import FixedRobotCfgPPO +from gym.envs.pendulum.pendulum_config import PendulumCfg + + +class PendulumPSDCfg(PendulumCfg): + class env(PendulumCfg.env): + num_envs = 1024 + episode_length_s = 10 + + class init_state(PendulumCfg.init_state): + reset_mode = "reset_to_uniform" + default_joint_angles = {"theta": 0.0} + dof_pos_range = { + "theta": [-torch.pi, torch.pi], + } + dof_vel_range = {"theta": [-5, 5]} + + class control(PendulumCfg.control): + ctrl_frequency = 10 + desired_sim_frequency = 100 + + class asset(PendulumCfg.asset): + joint_damping = 0.1 + + class reward_settings(PendulumCfg.reward_settings): + tracking_sigma = 0.25 + + class scaling(PendulumCfg.scaling): + dof_vel = 5.0 + dof_pos = 2.0 * torch.pi + tau_ff = 1.0 + toruqes = 2.5 # 5.0 + + +class PendulumPSDRunnerCfg(FixedRobotCfgPPO): + seed = -1 + runner_class_name = "PSACRunner" + + class actor: + hidden_dims = { + "latent": [128, 64], + "mean": [32], + "std": [32], + } + # * can be elu, relu, selu, crelu, lrelu, tanh, sigmoid + activation = { + "latent": "elu", + "mean": "elu", + "std": "elu", + } + + normalize_obs = False + obs = [ + "dof_pos_obs", + "dof_vel", + ] + actions = ["tau_ff"] + disable_actions = False + + class noise: + dof_pos = 0.0 + dof_vel = 0.0 + + class critic: + obs = ["dof_pos_obs", "dof_vel"] + hidden_dims = [16, 16] + # * can be elu, relu, selu, crelu, lrelu, tanh, sigmoid + activation = "elu" + # TODO[lm]: Current normalization uses torch.no_grad, this should be changed + normalize_obs = False + + # critic_class_name = "CholeskyLatent" + critic_class_name = "DenseSpectralLatent" + # * some class-specific params + minimize = False + relative_dim = 4 # 16 + latent_dim = 4 # 18, + dropouts = [0.1, 0.0] + latent_hidden_dims = [16] + latent_activation = "elu" + + class reward: + class weights: + theta = 0.0 + omega = 0.0 + equilibrium = 1.0 + energy = 0.5 + dof_vel = 0.0 + torques = 0.025 + + class termination_weight: + termination = 0.0 + + class algorithm(FixedRobotCfgPPO.algorithm): + initial_fill = 0 + storage_size = 100 * 1024 # steps_per_episode * num_envs + batch_size = 1024 # 4096 + max_gradient_steps = 10 # 10 # SB3: 1 + action_max = 2.0 + action_min = -2.0 + actor_noise_std = 1.0 + log_std_max = 4.0 + log_std_min = -20.0 + alpha = 0.2 + target_entropy = -1.0 + max_grad_norm = 1.0 + polyak = 0.995 # flipped compared to stable-baselines3 (polyak == 1-tau) + gamma = 0.99 + alpha_lr = 1e-4 + actor_lr = 1e-3 + critic_lr = 5e-4 + + class runner(FixedRobotCfgPPO.runner): + run_name = "" + experiment_name = "sac_pendulum" + max_iterations = 10_000 # number of policy updates + algorithm_class_name = "SAC" + num_steps_per_env = 1 + save_interval = 2000 + log_storage = True diff --git a/learning/modules/QRCritics.py b/learning/modules/QRCritics.py index a65ec304..3933f4e3 100644 --- a/learning/modules/QRCritics.py +++ b/learning/modules/QRCritics.py @@ -3,36 +3,14 @@ import torch.nn.functional as F from learning.modules.utils import RunningMeanStd, create_MLP - - -def create_lower_diagonal(x, n, device): - L = torch.zeros((*x.shape[:-1], n, n), device=device, requires_grad=False) - tril_indices = torch.tril_indices(row=n, col=n, offset=0) - rows, cols = tril_indices - L[..., rows, cols] = x - return L - - -def create_PD_lower_diagonal(x, n, device): - tril_indices = torch.tril_indices(row=n, col=n, offset=0) - diag_indices = (tril_indices[0] == tril_indices[1]).nonzero(as_tuple=True)[0] - x[..., diag_indices] = F.softplus(x[..., diag_indices]) - L = torch.zeros((*x.shape[:-1], n, n), device=device, requires_grad=False) - rows, cols = tril_indices - L[..., rows, cols] = x - return L - - -def compose_cholesky(L): - return torch.einsum("...ij, ...jk -> ...ik", L, L.transpose(-2, -1)) - - -def quadratify_xAx(x, A): - return torch.einsum( - "...ij, ...jk -> ...ik", - torch.einsum("...ij, ...jk -> ...ik", x.unsqueeze(-1).transpose(-2, -1), A), - x.unsqueeze(-1), - ).squeeze() +from learning.modules.utils import ( + quadratify_xAx, + compose_cholesky, + create_lower_diagonal, + least_squares_fit, + forward_affine, + create_PD_lower_diagonal, +) def init_weights(m): @@ -41,28 +19,6 @@ def init_weights(m): m.bias.data.fill_(0.01) -def least_squares_fit(x, y): - x_flat = x.view(-1, x.shape[-1]) - y_flat = y.view(-1, y.shape[-1]) - ones = torch.ones(x_flat.shape[0], 1, device=x.device) - x_aug = torch.cat([ones, x_flat], dim=1) - # Use torch.linalg.lstsq to find the least-squares solution - result = torch.linalg.lstsq(x_aug, y_flat) - coefficients = result.solution - bias = coefficients[0] - weights = coefficients[1:] - - return weights, bias - - -def forward_affine(x, weights, bias): - x_flat = x.view(-1, x.shape[-1]) - x_aug = torch.cat( - [torch.ones(x_flat.shape[0], 1, device=x_flat.device), x_flat], dim=1 - ) - return x_aug.matmul(torch.cat([bias.unsqueeze(0), weights], dim=0)) - - class Critic(nn.Module): def __init__( self, diff --git a/learning/modules/__init__.py b/learning/modules/__init__.py index 9505377d..8281ab8d 100644 --- a/learning/modules/__init__.py +++ b/learning/modules/__init__.py @@ -37,3 +37,4 @@ from .chimera_actor import ChimeraActor from .smooth_actor import SmoothActor from .QRCritics import * +from .TaylorCritics import * \ No newline at end of file diff --git a/learning/modules/utils/__init__.py b/learning/modules/utils/__init__.py index 1422b82d..35868ca0 100644 --- a/learning/modules/utils/__init__.py +++ b/learning/modules/utils/__init__.py @@ -1,2 +1,3 @@ from .neural_net import create_MLP, export_network from .normalize import RunningMeanStd +from .matfncs import create_lower_diagonal, create_PD_lower_diagonal, compose_cholesky, quadratify_xAx, least_squares_fit, forward_affine \ No newline at end of file diff --git a/learning/modules/utils/matfncs.py b/learning/modules/utils/matfncs.py new file mode 100644 index 00000000..92ebd0e7 --- /dev/null +++ b/learning/modules/utils/matfncs.py @@ -0,0 +1,53 @@ +import torch + + +def create_lower_diagonal(x, n, device): + L = torch.zeros((*x.shape[:-1], n, n), device=device, requires_grad=False) + tril_indices = torch.tril_indices(row=n, col=n, offset=0) + rows, cols = tril_indices + L[..., rows, cols] = x + return L + + +def create_PD_lower_diagonal(x, n, device): + tril_indices = torch.tril_indices(row=n, col=n, offset=0) + diag_indices = (tril_indices[0] == tril_indices[1]).nonzero(as_tuple=True)[0] + x[..., diag_indices] = F.softplus(x[..., diag_indices]) + L = torch.zeros((*x.shape[:-1], n, n), device=device, requires_grad=False) + rows, cols = tril_indices + L[..., rows, cols] = x + return L + + +def compose_cholesky(L): + return torch.einsum("...ij, ...jk -> ...ik", L, L.transpose(-2, -1)) + + +def quadratify_xAx(x, A): + return torch.einsum( + "...ij, ...jk -> ...ik", + torch.einsum("...ij, ...jk -> ...ik", x.unsqueeze(-1).transpose(-2, -1), A), + x.unsqueeze(-1), + ).squeeze() + + +def least_squares_fit(x, y): + x_flat = x.view(-1, x.shape[-1]) + y_flat = y.view(-1, y.shape[-1]) + ones = torch.ones(x_flat.shape[0], 1, device=x.device) + x_aug = torch.cat([ones, x_flat], dim=1) + # Use torch.linalg.lstsq to find the least-squares solution + result = torch.linalg.lstsq(x_aug, y_flat) + coefficients = result.solution + bias = coefficients[0] + weights = coefficients[1:] + + return weights, bias + + +def forward_affine(x, weights, bias): + x_flat = x.view(-1, x.shape[-1]) + x_aug = torch.cat( + [torch.ones(x_flat.shape[0], 1, device=x_flat.device), x_flat], dim=1 + ) + return x_aug.matmul(torch.cat([bias.unsqueeze(0), weights], dim=0)) From dbd48c9b5fc1555ffb88e0a8bd09488eeef8f38e Mon Sep 17 00:00:00 2001 From: Steve Heim Date: Tue, 3 Sep 2024 15:05:50 -0400 Subject: [PATCH 23/77] add layer norm to create_MLP, +kwargs flexibility --- learning/modules/actor.py | 2 +- learning/modules/critic.py | 2 +- learning/modules/utils/neural_net.py | 32 ++++++++++++++++++++++------ 3 files changed, 28 insertions(+), 8 deletions(-) diff --git a/learning/modules/actor.py b/learning/modules/actor.py index 14104df7..0f112dba 100644 --- a/learning/modules/actor.py +++ b/learning/modules/actor.py @@ -27,7 +27,7 @@ def __init__( self.num_actions = num_actions self.hidden_dims = hidden_dims self.activation = activation - self.NN = create_MLP(num_obs, num_actions, hidden_dims, activation) + self.NN = create_MLP(num_obs, num_actions, hidden_dims, activation, **kwargs) # Action noise self.std = nn.Parameter(init_noise_std * torch.ones(num_actions)) diff --git a/learning/modules/critic.py b/learning/modules/critic.py index f7960bfa..5dfad32d 100644 --- a/learning/modules/critic.py +++ b/learning/modules/critic.py @@ -16,7 +16,7 @@ def __init__( ): super().__init__() - self.NN = create_MLP(num_obs, 1, hidden_dims, activation) + self.NN = create_MLP(num_obs, 1, hidden_dims, activation, **kwargs) self._normalize_obs = normalize_obs if self._normalize_obs: self.obs_rms = RunningMeanStd(num_obs) diff --git a/learning/modules/utils/neural_net.py b/learning/modules/utils/neural_net.py index af782a53..27b47a54 100644 --- a/learning/modules/utils/neural_net.py +++ b/learning/modules/utils/neural_net.py @@ -4,18 +4,33 @@ def create_MLP( - num_inputs, num_outputs, hidden_dims, activations, dropouts=None, just_list=False + num_inputs, + num_outputs, + hidden_dims, + activations=None, + dropouts=0.0, + layer_norm=False, + just_list=False, + **kwargs, ): if not isinstance(activations, list): activations = [activations] * len(hidden_dims) - if dropouts is None: - dropouts = [0] * len(hidden_dims) - elif not isinstance(dropouts, list): + if not isinstance(dropouts, list): dropouts = [dropouts] * len(hidden_dims) + if not isinstance(layer_norm, list): + layer_norm = [layer_norm] * len(hidden_dims) + layers = [] # first layer if len(hidden_dims) > 0: - add_layer(layers, num_inputs, hidden_dims[0], activations[0], dropouts[0]) + add_layer( + layers, + num_inputs, + hidden_dims[0], + activations[0], + dropouts[0], + layer_norm[0], + ) for i in range(len(hidden_dims) - 1): add_layer( layers, @@ -23,6 +38,7 @@ def create_MLP( hidden_dims[i + 1], activations[i + 1], dropouts[i + 1], + layer_norm=layer_norm[i + 1], ) else: add_layer(layers, hidden_dims[-1], num_outputs) @@ -35,8 +51,12 @@ def create_MLP( return torch.nn.Sequential(*layers) -def add_layer(layer_list, num_inputs, num_outputs, activation=None, dropout=0): +def add_layer( + layer_list, num_inputs, num_outputs, activation=None, dropout=0, layer_norm=False +): layer_list.append(torch.nn.Linear(num_inputs, num_outputs)) + if layer_norm: + layer_list.append(torch.nn.LayerNorm(num_outputs)) if dropout > 0: layer_list.append(torch.nn.Dropout(p=dropout)) if activation is not None: From 7d970816aef13ea0c32af05e6e64f3687940d2bc Mon Sep 17 00:00:00 2001 From: Steve Heim Date: Tue, 3 Sep 2024 15:07:46 -0400 Subject: [PATCH 24/77] update for layernorm --- gym/envs/pendulum/pendulum_PSD_config.py | 34 +++++++++++------------- learning/modules/QRCritics.py | 24 ++++++++--------- learning/modules/chimera_actor.py | 20 +++++++------- learning/modules/state_estimator.py | 2 +- 4 files changed, 38 insertions(+), 42 deletions(-) diff --git a/gym/envs/pendulum/pendulum_PSD_config.py b/gym/envs/pendulum/pendulum_PSD_config.py index 3de1ccca..056070a4 100644 --- a/gym/envs/pendulum/pendulum_PSD_config.py +++ b/gym/envs/pendulum/pendulum_PSD_config.py @@ -27,10 +27,10 @@ class reward_settings(PendulumCfg.reward_settings): tracking_sigma = 0.25 class scaling(PendulumCfg.scaling): - dof_vel = 5.0 - dof_pos = 2.0 * torch.pi + dof_vel = 25.0 + dof_pos = 10.0 * torch.pi tau_ff = 1.0 - toruqes = 2.5 # 5.0 + torques = 2.5 # 5.0 class PendulumPSDRunnerCfg(FixedRobotCfgPPO): @@ -38,17 +38,14 @@ class PendulumPSDRunnerCfg(FixedRobotCfgPPO): runner_class_name = "PSACRunner" class actor: - hidden_dims = { - "latent": [128, 64], - "mean": [32], - "std": [32], - } - # * can be elu, relu, selu, crelu, lrelu, tanh, sigmoid - activation = { - "latent": "elu", - "mean": "elu", - "std": "elu", - } + latent_nn = {"hidden_dims": [128, 64], "activation": "elu", "layer_norm": True} + mean_nn = {"hidden_dims": [32], "activation": "elu", "layer_norm": True} + std_nn = {"hidden_dims": [32], "activation": "elu", "layer_norm": True} + nn_params = {"latent": latent_nn, "mean": mean_nn, "std": std_nn} + # hidden_dims = {"latent": [128, 64], "mean": [32], "std": [32]} + # # * can be elu, relu, selu, crelu, lrelu, tanh, sigmoid + # activation = {"latent": "elu", "mean": "elu", "std": "elu"} + # layer_norm = {"latent": True, "mean": True, "std": True} normalize_obs = False obs = [ @@ -66,17 +63,18 @@ class critic: obs = ["dof_pos_obs", "dof_vel"] hidden_dims = [16, 16] # * can be elu, relu, selu, crelu, lrelu, tanh, sigmoid + dropouts = [0.1, 0.0] + layer_norm = [True, True] activation = "elu" # TODO[lm]: Current normalization uses torch.no_grad, this should be changed normalize_obs = False # critic_class_name = "CholeskyLatent" - critic_class_name = "DenseSpectralLatent" + critic_class_name = "Critic" # * some class-specific params minimize = False relative_dim = 4 # 16 latent_dim = 4 # 18, - dropouts = [0.1, 0.0] latent_hidden_dims = [16] latent_activation = "elu" @@ -114,8 +112,8 @@ class algorithm(FixedRobotCfgPPO.algorithm): class runner(FixedRobotCfgPPO.runner): run_name = "" experiment_name = "sac_pendulum" - max_iterations = 10_000 # number of policy updates + max_iterations = 5_000 # number of policy updates algorithm_class_name = "SAC" num_steps_per_env = 1 - save_interval = 2000 + save_interval = 500 log_storage = True diff --git a/learning/modules/QRCritics.py b/learning/modules/QRCritics.py index 3933f4e3..d2ed9378 100644 --- a/learning/modules/QRCritics.py +++ b/learning/modules/QRCritics.py @@ -31,7 +31,7 @@ def __init__( ): super().__init__() - self.NN = create_MLP(num_obs, 1, hidden_dims, activation) + self.NN = create_MLP(num_obs, 1, hidden_dims, activation, **kwargs) self._normalize_obs = normalize_obs if self._normalize_obs: self.obs_rms = RunningMeanStd(num_obs) @@ -61,7 +61,7 @@ def __init__( hidden_dims, latent_dim=None, activation="elu", - dropouts=None, + dropouts=0.0, normalize_obs=False, minimize=False, device="cuda", @@ -118,7 +118,7 @@ def __init__( hidden_dims, latent_dim=None, activation="elu", - dropouts=None, + dropouts=0.0, normalize_obs=False, minimize=False, latent_hidden_dims=[128, 128], @@ -163,7 +163,7 @@ def __init__( hidden_dims, latent_dim=None, activation="elu", - dropouts=None, + dropouts=0.0, normalize_obs=False, minimize=False, device="cuda", @@ -223,9 +223,9 @@ def __init__( latent_dim, latent_hidden_dims=[64], latent_activation="tanh", - latent_dropouts=None, + latent_dropouts=0.0, activation="elu", - dropouts=None, + dropouts=0.0, normalize_obs=False, minimize=False, device="cuda", @@ -314,13 +314,13 @@ def __init__( num_obs, hidden_dims, activation="elu", - dropouts=None, + dropouts=0.0, normalize_obs=False, relative_dim=None, latent_dim=None, latent_hidden_dims=[64], latent_activation="tanh", - latent_dropouts=None, + latent_dropouts=0.0, minimize=False, device="cuda", **kwargs, @@ -414,13 +414,13 @@ def __init__( num_obs, hidden_dims, activation="elu", - dropouts=None, + dropouts=0.0, normalize_obs=False, relative_dim=None, latent_dim=None, latent_hidden_dims=[64], latent_activation="tanh", - latent_dropouts=None, + latent_dropouts=0.0, minimize=False, device="cuda", **kwargs, @@ -531,7 +531,7 @@ def __init__( action_dim, latent_dim=None, activation="elu", - dropouts=None, + dropouts=0.0, normalize_obs=False, minimize=False, device="cuda", @@ -643,7 +643,7 @@ def __init__( hidden_dims, latent_dim=None, activation="elu", - dropouts=None, + dropouts=0.0, normalize_obs=False, minimize=False, device="cuda", diff --git a/learning/modules/chimera_actor.py b/learning/modules/chimera_actor.py index a9ad77d6..1d5ead41 100644 --- a/learning/modules/chimera_actor.py +++ b/learning/modules/chimera_actor.py @@ -11,8 +11,9 @@ def __init__( self, num_obs, num_actions, - hidden_dims, - activation, + # hidden_dims, + # activation, + nn_params, std_init=1.0, log_std_max=4.0, log_std_min=-20.0, @@ -34,21 +35,18 @@ def __init__( self.latent_NN = create_MLP( num_inputs=num_obs, - num_outputs=hidden_dims["latent"][-1], - hidden_dims=hidden_dims["latent"][:-1], - activations=activation["latent"], + num_outputs=nn_params["latent"]["hidden_dims"][-1], + **nn_params["latent"], ) self.mean_NN = create_MLP( - num_inputs=hidden_dims["latent"][-1], + num_inputs=nn_params["latent"]["hidden_dims"][-1], num_outputs=num_actions, - hidden_dims=hidden_dims["mean"], - activations=activation["mean"], + **nn_params["mean"], ) self.std_NN = create_MLP( - num_inputs=hidden_dims["latent"][-1], + num_inputs=nn_params["latent"]["hidden_dims"][-1], num_outputs=num_actions, - hidden_dims=hidden_dims["std"], - activations=activation["std"], + **nn_params["std"], ) # maybe zap diff --git a/learning/modules/state_estimator.py b/learning/modules/state_estimator.py index f8cfe221..dfba137d 100644 --- a/learning/modules/state_estimator.py +++ b/learning/modules/state_estimator.py @@ -20,7 +20,7 @@ def __init__( num_outputs, hidden_dims=[256, 128], activation="elu", - dropouts=None, + dropouts=0.0, **kwargs, ): if kwargs: From be2e990c6ec3d2887e3f89e856e66d5284f5bdad Mon Sep 17 00:00:00 2001 From: Steve Heim Date: Wed, 4 Sep 2024 09:58:49 -0400 Subject: [PATCH 25/77] wip --- gym/envs/mit_humanoid/lander.py | 16 +++++++++++++++ gym/envs/mit_humanoid/lander_config.py | 28 +++++++++----------------- 2 files changed, 25 insertions(+), 19 deletions(-) diff --git a/gym/envs/mit_humanoid/lander.py b/gym/envs/mit_humanoid/lander.py index 972eb413..f86d30f4 100644 --- a/gym/envs/mit_humanoid/lander.py +++ b/gym/envs/mit_humanoid/lander.py @@ -2,12 +2,28 @@ # from gym.envs.base.legged_robot import LeggedRobot from gym.envs.mit_humanoid.mit_humanoid import MIT_Humanoid +from isaacgym.torch_utils import torch_rand_float class Lander(MIT_Humanoid): def __init__(self, gym, sim, cfg, sim_params, sim_device, headless): super().__init__(gym, sim, cfg, sim_params, sim_device, headless) + def _resample_commands(self, env_ids): + """Randommly select commands of some environments + + Args: + env_ids (List[int]): Environments ids for which new commands are needed + """ + if len(env_ids) == 0: + return + super()._resample_commands(env_ids) + # * with 75% chance, reset to 0 + self.commands[env_ids, :] *= ( + torch_rand_float(0, 1, (len(env_ids), 1), device=self.device).squeeze(1) + < 0.25 + ).unsqueeze(1) + # --- rewards --- def _switch(self, mode=None): diff --git a/gym/envs/mit_humanoid/lander_config.py b/gym/envs/mit_humanoid/lander_config.py index 0de34b4c..8219d459 100644 --- a/gym/envs/mit_humanoid/lander_config.py +++ b/gym/envs/mit_humanoid/lander_config.py @@ -112,7 +112,7 @@ class control(LeggedRobotCfg.control): } # [N*m*s/rad] ctrl_frequency = 100 - desired_sim_frequency = 1000 + desired_sim_frequency = 500 # class oscillator: # base_frequency = 3.0 # [Hz] @@ -121,13 +121,13 @@ class commands: resampling_time = 10.0 # time before command are changed[s] class ranges: - lin_vel_x = [-0.0, 0.0] # min max [m/s] [-0.75, 0.75] - lin_vel_y = 0.0 # max [m/s] - yaw_vel = 0.0 # max [rad/s] + lin_vel_x = [-2.0, 2.0] # min max [m/s] [-0.75, 0.75] + lin_vel_y = 0.3 # max [m/s] + yaw_vel = 1.0 # max [rad/s] class push_robots: toggle = True - interval_s = 1 + interval_s = 2 max_push_vel_xy = 0.5 push_box_dims = [0.1, 0.1, 0.3] # x,y,z [m] @@ -146,13 +146,9 @@ class asset(LeggedRobotCfg.asset): foot_name = "foot" penalize_contacts_on = ["arm", "hand", "shoulder"] terminate_after_contacts_on = ["base"] - end_effector_names = ["hand", "foot"] # ?? flip_visual_attachments = False self_collisions = 0 # 1 to disagble, 0 to enable...bitwise filter collapse_fixed_joints = False - # * see GymDofDriveModeFlags - # * (0 is none, 1 is pos tgt, 2 is vel tgt, 3 effort) - default_dof_drive_mode = 3 fix_base_link = False disable_gravity = False disable_motors = False @@ -279,24 +275,18 @@ class critic(LeggedRobotRunnerCfg.critic): class reward: class weights: - # tracking_lin_vel = 0.0 - # tracking_ang_vel = 1.5 - # orientation = 1.0 torques = 5.0e-5 - power = 0 # 1.0e-2 + power = 1e-6 # 1.0e-2 min_base_height = 1.5 + lin_vel_xy = 1.0 action_rate = 1e-3 action_rate2 = 1e-3 lin_vel_z = 0.0 ang_vel_xy = 0.0 dof_vel = 0.5 - # stand_still = 0.25 dof_pos_limits = 0.25 - dof_near_home = 0.25 - # stance = 1.0 - # swing = 1.0 + dof_near_home = 0.75 hips_forward = 0.0 - # walk_freq = 0.0 # 2.5 collision = 1.0 class termination_weight: @@ -330,6 +320,6 @@ class runner(LeggedRobotRunnerCfg.runner): algorithm_class_name = "PPO2" num_steps_per_env = 24 max_iterations = 1000 - run_name = "Standing" + run_name = "lander" experiment_name = "Humanoid" save_interval = 50 From 27991100e4388ea3df415b75f4b022a599747b51 Mon Sep 17 00:00:00 2001 From: Steve Heim Date: Wed, 4 Sep 2024 15:42:46 -0400 Subject: [PATCH 26/77] Refactor for action frequency handled in runner only for lander: needs to overload _check_terminations_and_timeouts to not actually reset so that reset can be handled only by runner (without breaking backwards compatibility) Also keeps a list of rewards, for finer reward integration, and can also do traj stats --- gym/envs/mit_humanoid/lander.py | 7 ++ gym/envs/mit_humanoid/lander_config.py | 15 +-- gym/utils/helpers.py | 2 +- learning/runners/my_runner.py | 128 +++++++++++++++++++++---- 4 files changed, 127 insertions(+), 25 deletions(-) diff --git a/gym/envs/mit_humanoid/lander.py b/gym/envs/mit_humanoid/lander.py index f86d30f4..86d37321 100644 --- a/gym/envs/mit_humanoid/lander.py +++ b/gym/envs/mit_humanoid/lander.py @@ -24,6 +24,13 @@ def _resample_commands(self, env_ids): < 0.25 ).unsqueeze(1) + def _check_terminations_and_timeouts(self): + """Check if environments need to be reset""" + contact_forces = self.contact_forces[:, self.termination_contact_indices, :] + self.terminated |= torch.any(torch.norm(contact_forces, dim=-1) > 1.0, dim=1) + self.timed_out = self.episode_length_buf >= self.max_episode_length + # self.to_be_reset = self.timed_out | self.terminated + # --- rewards --- def _switch(self, mode=None): diff --git a/gym/envs/mit_humanoid/lander_config.py b/gym/envs/mit_humanoid/lander_config.py index 8219d459..48c2eb53 100644 --- a/gym/envs/mit_humanoid/lander_config.py +++ b/gym/envs/mit_humanoid/lander_config.py @@ -111,7 +111,7 @@ class control(LeggedRobotCfg.control): "elbow": 1.0, } # [N*m*s/rad] - ctrl_frequency = 100 + ctrl_frequency = 500 desired_sim_frequency = 500 # class oscillator: @@ -218,13 +218,15 @@ class scaling(LeggedRobotCfg.scaling): class LanderRunnerCfg(LeggedRobotRunnerCfg): seed = -1 - runner_class_name = "OnPolicyRunner" + runner_class_name = "MyRunner" class actor(LeggedRobotRunnerCfg.actor): + frequency = 100 init_noise_std = 1.0 hidden_dims = [512, 256, 128] # * can be elu, relu, selu, crelu, lrelu, tanh, sigmoid - activation = "tanh" + activation = ["elu", "elu", "tanh"] + layer_norm = True smooth_exploration = False obs = [ @@ -256,7 +258,8 @@ class noise: class critic(LeggedRobotRunnerCfg.critic): hidden_dims = [512, 256, 128] # * can be elu, relu, selu, crelu, lrelu, tanh, sigmoid - activation = "tanh" + activation = "elu" + layer_norm = True obs = [ # "base_height", @@ -290,7 +293,7 @@ class weights: collision = 1.0 class termination_weight: - termination = 15 + termination = 1.0 class algorithm(LeggedRobotRunnerCfg.algorithm): # both @@ -319,7 +322,7 @@ class runner(LeggedRobotRunnerCfg.runner): policy_class_name = "ActorCritic" algorithm_class_name = "PPO2" num_steps_per_env = 24 - max_iterations = 1000 + max_iterations = 500 run_name = "lander" experiment_name = "Humanoid" save_interval = 50 diff --git a/gym/utils/helpers.py b/gym/utils/helpers.py index 90af8bce..8e74d93c 100644 --- a/gym/utils/helpers.py +++ b/gym/utils/helpers.py @@ -191,7 +191,7 @@ def get_args(custom_parameters=None): { "name": "--task", "type": str, - "default": "cartpole", + "default": "lander", "help": "Resume training or start testing from a checkpoint. " "Overrides config file if provided.", }, diff --git a/learning/runners/my_runner.py b/learning/runners/my_runner.py index d97dee8d..96eedc77 100644 --- a/learning/runners/my_runner.py +++ b/learning/runners/my_runner.py @@ -19,14 +19,18 @@ def __init__(self, env, train_cfg, device="cpu"): self.device, ) - def learn(self): + def learn(self, states_to_log_dict=None): self.set_up_logger() + n_policy_steps = int((1 / self.env.dt) / self.actor_cfg["frequency"]) + assert n_policy_steps > 0, "actor frequency should be less than ctrl_freq" + rewards_dict = {} + rewards_list = [dict() for x in range(n_policy_steps)] self.alg.switch_to_train() - actor_obs = self.get_obs(self.actor_cfg["actor_obs"]) - critic_obs = self.get_obs(self.critic_cfg["critic_obs"]) + actor_obs = self.get_obs(self.actor_cfg["obs"]) + critic_obs = self.get_obs(self.critic_cfg["obs"]) tot_iter = self.it + self.num_learning_iterations self.save() @@ -35,8 +39,10 @@ def learn(self): transition.update( { "actor_obs": actor_obs, - "actions": self.alg.act(actor_obs, critic_obs), + "next_actor_obs": actor_obs, + "actions": self.alg.act(actor_obs), "critic_obs": critic_obs, + "next_critic_obs": critic_obs, "rewards": self.get_rewards({"termination": 0.0})["termination"], "dones": self.get_timed_out(), } @@ -52,10 +58,17 @@ def learn(self): for self.it in range(self.it + 1, tot_iter + 1): logger.tic("iteration") logger.tic("collection") + + # * Simulate environment and log states + if states_to_log_dict is not None: + it_idx = self.it - 1 + if it_idx % 10 == 0: + self.sim_and_log_states(states_to_log_dict, it_idx) + # * Rollout with torch.inference_mode(): for i in range(self.num_steps_per_env): - actions = self.alg.act(actor_obs, critic_obs) + actions = self.alg.act(actor_obs) self.set_actions( self.actor_cfg["actions"], actions, @@ -69,33 +82,48 @@ def learn(self): "critic_obs": critic_obs, } ) - - self.env.step() + for step in range(n_policy_steps): + self.env.step() + # put reward integration here + self.update_rewards(rewards_list[step], self.env.terminated) + # handle time-outs/terminations happening inside here with mask + else: + # catch and reset failed envs + to_be_reset = self.env.timed_out | self.env.terminated + env_ids = (to_be_reset).nonzero(as_tuple=False).flatten() + self.env._reset_idx(env_ids) actor_obs = self.get_noisy_obs( - self.actor_cfg["actor_obs"], self.actor_cfg["noise"] + self.actor_cfg["obs"], self.actor_cfg["noise"] ) - critic_obs = self.get_obs(self.critic_cfg["critic_obs"]) + critic_obs = self.get_obs(self.critic_cfg["obs"]) + # * get time_outs - timed_out = self.get_timed_out() - terminated = self.get_terminated() - dones = timed_out | terminated - self.update_rewards(rewards_dict, terminated) - total_rewards = torch.stack(tuple(rewards_dict.values())).sum(dim=0) + self.update_rewards(rewards_dict, self.env.terminated) + + # integrate rewards from list + total_rewards = torch.stack( + tuple( + torch.stack(tuple(rewards_dict.values())) + for rewards_dict in rewards_list + ) + ).sum(dim=(0, 1)) transition.update( { + "next_actor_obs": actor_obs, + "next_critic_obs": critic_obs, "rewards": total_rewards, - "timed_out": timed_out, - "dones": dones, + "timed_out": self.env.timed_out, + "dones": self.env.timed_out | self.env.terminated, } ) storage.add_transitions(transition) logger.log_rewards(rewards_dict) logger.log_rewards({"total_rewards": total_rewards}) - logger.finish_step(dones) + logger.finish_step(self.env.timed_out | self.env.terminated) logger.toc("collection") logger.tic("learning") @@ -113,6 +141,21 @@ def learn(self): self.save() self.save() + @torch.no_grad + def burn_in_normalization(self, n_iterations=100): + actor_obs = self.get_obs(self.actor_cfg["obs"]) + critic_obs = self.get_obs(self.critic_cfg["obs"]) + for _ in range(n_iterations): + actions = self.alg.act(actor_obs) + self.set_actions(self.actor_cfg["actions"], actions) + self.env.step() + actor_obs = self.get_noisy_obs( + self.actor_cfg["obs"], self.actor_cfg["noise"] + ) + critic_obs = self.get_obs(self.critic_cfg["obs"]) + self.alg.critic.evaluate(critic_obs) + self.env.reset() + def set_up_logger(self): logger.register_rewards(list(self.critic_cfg["reward"]["weights"].keys())) logger.register_rewards( @@ -120,8 +163,57 @@ def set_up_logger(self): ) logger.register_rewards(["total_rewards"]) logger.register_category( - "algorithm", self.alg, ["mean_value_loss", "mean_surrogate_loss"] + "algorithm", + self.alg, + ["mean_value_loss", "mean_surrogate_loss", "learning_rate"], ) logger.register_category("actor", self.alg.actor, ["action_std", "entropy"]) logger.attach_torch_obj_to_wandb((self.alg.actor, self.alg.critic)) + + def update_rewards(self, rewards_dict, terminated): + # sum existing rewards with new rewards + + rewards_dict.update( + self.get_rewards( + self.critic_cfg["reward"]["termination_weight"], mask=terminated + ) + ) + rewards_dict.update( + self.get_rewards( + self.critic_cfg["reward"]["weights"], + modifier=self.env.dt, + mask=~terminated, + ) + ) + + def sim_and_log_states(self, states_to_log_dict, it_idx): + # Simulate environment for as many steps as expected in the dict. + # Log states to the dict, as well as whether the env terminated. + steps = states_to_log_dict["terminated"].shape[2] + actor_obs = self.get_obs(self.policy_cfg["actor_obs"]) + + with torch.inference_mode(): + for i in range(steps): + actions = self.alg.act(actor_obs) + self.set_actions( + self.policy_cfg["actions"], + actions, + self.policy_cfg["disable_actions"], + ) + + self.env.step() + + actor_obs = self.get_noisy_obs( + self.policy_cfg["actor_obs"], self.policy_cfg["noise"] + ) + + # Log states (just for the first env) + terminated = self.get_terminated()[0] + for state in states_to_log_dict: + if state == "terminated": + states_to_log_dict[state][0, it_idx, i, :] = terminated + else: + states_to_log_dict[state][0, it_idx, i, :] = getattr( + self.env, state + )[0, :] From 0979fab4e98b83c6a1132dae002e08ff22d57cea Mon Sep 17 00:00:00 2001 From: Steve Heim Date: Fri, 6 Sep 2024 16:31:38 -0400 Subject: [PATCH 27/77] working SAC on pendulum, ready for merge --- gym/envs/pendulum/pendulum_PSD_config.py | 107 ++++++----------------- gym/envs/pendulum/pendulum_SAC_config.py | 15 +--- learning/runners/psd_sac_runner.py | 1 + scripts/train.py | 1 - 4 files changed, 30 insertions(+), 94 deletions(-) diff --git a/gym/envs/pendulum/pendulum_PSD_config.py b/gym/envs/pendulum/pendulum_PSD_config.py index 056070a4..083588c8 100644 --- a/gym/envs/pendulum/pendulum_PSD_config.py +++ b/gym/envs/pendulum/pendulum_PSD_config.py @@ -1,65 +1,34 @@ -import torch -from gym.envs.base.fixed_robot_config import FixedRobotCfgPPO -from gym.envs.pendulum.pendulum_config import PendulumCfg +from gym.envs.pendulum.pendulum_SAC_config import PendulumSACCfg, PendulumSACRunnerCfg -class PendulumPSDCfg(PendulumCfg): - class env(PendulumCfg.env): - num_envs = 1024 - episode_length_s = 10 +class PendulumPSDCfg(PendulumSACCfg): + class env(PendulumSACCfg.env): + pass - class init_state(PendulumCfg.init_state): - reset_mode = "reset_to_uniform" - default_joint_angles = {"theta": 0.0} - dof_pos_range = { - "theta": [-torch.pi, torch.pi], - } - dof_vel_range = {"theta": [-5, 5]} + class init_state(PendulumSACCfg.init_state): + pass - class control(PendulumCfg.control): - ctrl_frequency = 10 - desired_sim_frequency = 100 + class control(PendulumSACCfg.control): + pass - class asset(PendulumCfg.asset): - joint_damping = 0.1 + class asset(PendulumSACCfg.asset): + pass - class reward_settings(PendulumCfg.reward_settings): - tracking_sigma = 0.25 + class reward_settings(PendulumSACCfg.reward_settings): + pass - class scaling(PendulumCfg.scaling): - dof_vel = 25.0 - dof_pos = 10.0 * torch.pi - tau_ff = 1.0 - torques = 2.5 # 5.0 + class scaling(PendulumSACCfg.scaling): + pass -class PendulumPSDRunnerCfg(FixedRobotCfgPPO): +class PendulumPSDRunnerCfg(PendulumSACRunnerCfg): seed = -1 runner_class_name = "PSACRunner" - class actor: - latent_nn = {"hidden_dims": [128, 64], "activation": "elu", "layer_norm": True} - mean_nn = {"hidden_dims": [32], "activation": "elu", "layer_norm": True} - std_nn = {"hidden_dims": [32], "activation": "elu", "layer_norm": True} - nn_params = {"latent": latent_nn, "mean": mean_nn, "std": std_nn} - # hidden_dims = {"latent": [128, 64], "mean": [32], "std": [32]} - # # * can be elu, relu, selu, crelu, lrelu, tanh, sigmoid - # activation = {"latent": "elu", "mean": "elu", "std": "elu"} - # layer_norm = {"latent": True, "mean": True, "std": True} + class actor(PendulumSACRunnerCfg.actor): + pass - normalize_obs = False - obs = [ - "dof_pos_obs", - "dof_vel", - ] - actions = ["tau_ff"] - disable_actions = False - - class noise: - dof_pos = 0.0 - dof_vel = 0.0 - - class critic: + class critic(PendulumSACRunnerCfg.critic): obs = ["dof_pos_obs", "dof_vel"] hidden_dims = [16, 16] # * can be elu, relu, selu, crelu, lrelu, tanh, sigmoid @@ -70,7 +39,8 @@ class critic: normalize_obs = False # critic_class_name = "CholeskyLatent" - critic_class_name = "Critic" + # critic_class_name = "Critic" + critic_class_name = "DenseSpectralLatent" # * some class-specific params minimize = False relative_dim = 4 # 16 @@ -78,41 +48,14 @@ class critic: latent_hidden_dims = [16] latent_activation = "elu" - class reward: - class weights: - theta = 0.0 - omega = 0.0 - equilibrium = 1.0 - energy = 0.5 - dof_vel = 0.0 - torques = 0.025 - - class termination_weight: - termination = 0.0 - - class algorithm(FixedRobotCfgPPO.algorithm): - initial_fill = 0 - storage_size = 100 * 1024 # steps_per_episode * num_envs - batch_size = 1024 # 4096 - max_gradient_steps = 10 # 10 # SB3: 1 - action_max = 2.0 - action_min = -2.0 - actor_noise_std = 1.0 - log_std_max = 4.0 - log_std_min = -20.0 - alpha = 0.2 - target_entropy = -1.0 - max_grad_norm = 1.0 - polyak = 0.995 # flipped compared to stable-baselines3 (polyak == 1-tau) - gamma = 0.99 + class algorithm(PendulumSACRunnerCfg.algorithm): alpha_lr = 1e-4 - actor_lr = 1e-3 - critic_lr = 5e-4 + actor_lr = 1e-4 + critic_lr = 1e-4 - class runner(FixedRobotCfgPPO.runner): + class runner(PendulumSACRunnerCfg.runner): run_name = "" - experiment_name = "sac_pendulum" - max_iterations = 5_000 # number of policy updates + experiment_name = "psd_pendulum" algorithm_class_name = "SAC" num_steps_per_env = 1 save_interval = 500 diff --git a/gym/envs/pendulum/pendulum_SAC_config.py b/gym/envs/pendulum/pendulum_SAC_config.py index b094ee91..0125cee4 100644 --- a/gym/envs/pendulum/pendulum_SAC_config.py +++ b/gym/envs/pendulum/pendulum_SAC_config.py @@ -37,17 +37,10 @@ class PendulumSACRunnerCfg(FixedRobotCfgPPO): runner_class_name = "OffPolicyRunner" class actor(FixedRobotCfgPPO.actor): - hidden_dims = { - "latent": [128, 64], - "mean": [32], - "std": [32], - } - # * can be elu, relu, selu, crelu, lrelu, tanh, sigmoid - activation = { - "latent": "elu", - "mean": "elu", - "std": "elu", - } + latent_nn = {"hidden_dims": [128, 64], "activation": "elu", "layer_norm": True} + mean_nn = {"hidden_dims": [32], "activation": "elu", "layer_norm": True} + std_nn = {"hidden_dims": [32], "activation": "elu", "layer_norm": True} + nn_params = {"latent": latent_nn, "mean": mean_nn, "std": std_nn} normalize_obs = False obs = [ diff --git a/learning/runners/psd_sac_runner.py b/learning/runners/psd_sac_runner.py index 0ba89104..57b411a1 100644 --- a/learning/runners/psd_sac_runner.py +++ b/learning/runners/psd_sac_runner.py @@ -7,6 +7,7 @@ from learning.runners import OffPolicyRunner from learning.modules import Critic, ChimeraActor from learning.modules.QRCritics import * # noqa F401 +from learning.modules.TaylorCritics import * # noqa F401 from learning.storage import ReplayBuffer from learning.algorithms import SAC diff --git a/scripts/train.py b/scripts/train.py index a2535408..759992cf 100644 --- a/scripts/train.py +++ b/scripts/train.py @@ -31,4 +31,3 @@ def train(train_cfg, policy_runner): if __name__ == "__main__": train_cfg, policy_runner = setup() train(train_cfg=train_cfg, policy_runner=policy_runner) - print(f"Final value offset: {policy_runner.alg.critic.value_offset.item()}") From 1d3251fb52688e3266dec77fb8b6229b29428e9e Mon Sep 17 00:00:00 2001 From: Steve Heim Date: Sun, 8 Sep 2024 16:52:16 -0400 Subject: [PATCH 28/77] hotfix: uncommited imports in init --- learning/modules/__init__.py | 1 - learning/runners/psd_sac_runner.py | 7 +------ 2 files changed, 1 insertion(+), 7 deletions(-) diff --git a/learning/modules/__init__.py b/learning/modules/__init__.py index 8281ab8d..9505377d 100644 --- a/learning/modules/__init__.py +++ b/learning/modules/__init__.py @@ -37,4 +37,3 @@ from .chimera_actor import ChimeraActor from .smooth_actor import SmoothActor from .QRCritics import * -from .TaylorCritics import * \ No newline at end of file diff --git a/learning/runners/psd_sac_runner.py b/learning/runners/psd_sac_runner.py index 57b411a1..31afda18 100644 --- a/learning/runners/psd_sac_runner.py +++ b/learning/runners/psd_sac_runner.py @@ -1,13 +1,8 @@ -import os -import torch -from tensordict import TensorDict - from learning.utils import Logger from learning.runners import OffPolicyRunner -from learning.modules import Critic, ChimeraActor +from learning.modules import Critic, ChimeraActor # noqa F401 from learning.modules.QRCritics import * # noqa F401 -from learning.modules.TaylorCritics import * # noqa F401 from learning.storage import ReplayBuffer from learning.algorithms import SAC From f105c0eabc784fa5f93e487f29a78fe752d3af47 Mon Sep 17 00:00:00 2001 From: Steve Heim Date: Mon, 9 Sep 2024 09:31:47 -0400 Subject: [PATCH 29/77] update nn_params --- gym/envs/pendulum/pendulum_SAC_config.py | 5 +---- 1 file changed, 1 insertion(+), 4 deletions(-) diff --git a/gym/envs/pendulum/pendulum_SAC_config.py b/gym/envs/pendulum/pendulum_SAC_config.py index 0125cee4..836a6185 100644 --- a/gym/envs/pendulum/pendulum_SAC_config.py +++ b/gym/envs/pendulum/pendulum_SAC_config.py @@ -43,10 +43,7 @@ class actor(FixedRobotCfgPPO.actor): nn_params = {"latent": latent_nn, "mean": mean_nn, "std": std_nn} normalize_obs = False - obs = [ - "dof_pos_obs", - "dof_vel", - ] + obs = ["dof_pos_obs", "dof_vel"] actions = ["tau_ff"] disable_actions = False From c04b563f03a540a599fdee1522667e1eb4b7dda9 Mon Sep 17 00:00:00 2001 From: Steve Heim Date: Mon, 9 Sep 2024 14:06:18 -0400 Subject: [PATCH 30/77] apply stash w/ sim-freq reward sampling. Adjust some reward weights of action_rate (mini-cheetah) to work decently. --- gym/envs/base/task_skeleton.py | 8 +- .../mini_cheetah/mini_cheetah_osc_config.py | 7 +- gym/envs/mini_cheetah/mini_cheetah_ref.py | 9 ++ .../mini_cheetah/mini_cheetah_ref_config.py | 9 +- gym/envs/mit_humanoid/lander_config.py | 2 +- gym/utils/task_registry.py | 3 + learning/runners/my_runner.py | 57 +++++++---- learning/runners/off_policy_runner.py | 95 ++++++++++++------- learning/runners/on_policy_runner.py | 69 +++++++++----- 9 files changed, 178 insertions(+), 81 deletions(-) diff --git a/gym/envs/base/task_skeleton.py b/gym/envs/base/task_skeleton.py index 0974970a..4f931eab 100644 --- a/gym/envs/base/task_skeleton.py +++ b/gym/envs/base/task_skeleton.py @@ -5,10 +5,14 @@ class TaskSkeleton: - def __init__(self, num_envs=1, max_episode_length=1.0, device="cpu"): + def __init__( + self, num_envs=1, max_episode_length=1.0, device="cpu", auto_reset=True + ): self.num_envs = num_envs self.max_episode_length = max_episode_length self.device = device + self.auto_reset = auto_reset + return None def get_states(self, obs_list): @@ -70,7 +74,7 @@ def _check_terminations_and_timeouts(self): contact_forces = self.contact_forces[:, self.termination_contact_indices, :] self.terminated |= torch.any(torch.norm(contact_forces, dim=-1) > 1.0, dim=1) self.timed_out = self.episode_length_buf >= self.max_episode_length - self.to_be_reset = self.timed_out | self.terminated + # self.to_be_reset = self.timed_out | self.terminated def step(self, actions): raise NotImplementedError diff --git a/gym/envs/mini_cheetah/mini_cheetah_osc_config.py b/gym/envs/mini_cheetah/mini_cheetah_osc_config.py index ec283033..34996404 100644 --- a/gym/envs/mini_cheetah/mini_cheetah_osc_config.py +++ b/gym/envs/mini_cheetah/mini_cheetah_osc_config.py @@ -64,7 +64,7 @@ class control(MiniCheetahCfg.control): # * PD Drive parameters: stiffness = {"haa": 20.0, "hfe": 20.0, "kfe": 20.0} damping = {"haa": 0.5, "hfe": 0.5, "kfe": 0.5} - ctrl_frequency = 100 + ctrl_frequency = 500 desired_sim_frequency = 500 class osc: @@ -163,6 +163,7 @@ class MiniCheetahOscRunnerCfg(MiniCheetahRunnerCfg): runner_class_name = "OnPolicyRunner" class actor(MiniCheetahRunnerCfg.actor): + frequency = 100 hidden_dims = [256, 256, 128] # * can be elu, relu, selu, crelu, lrelu, tanh, sigmoid activation = "elu" @@ -221,8 +222,8 @@ class weights: dof_vel = 0.0 min_base_height = 1.5 collision = 0 - action_rate = 0.1 # -0.01 - action_rate2 = 0.01 # -0.001 + action_rate = 0.001 # -0.01 + action_rate2 = 0.0001 # -0.001 stand_still = 0.0 dof_pos_limits = 0.0 feet_contact_forces = 0.0 diff --git a/gym/envs/mini_cheetah/mini_cheetah_ref.py b/gym/envs/mini_cheetah/mini_cheetah_ref.py index 57e1f0e7..d3f8a89b 100644 --- a/gym/envs/mini_cheetah/mini_cheetah_ref.py +++ b/gym/envs/mini_cheetah/mini_cheetah_ref.py @@ -50,6 +50,15 @@ def _resample_commands(self, env_ids): ).squeeze(1) self.commands[env_ids, :3] *= (rand_ids < 0.9).unsqueeze(1) + def _check_terminations_and_timeouts(self): + """Check if environments need to be reset""" + contact_forces = self.contact_forces[:, self.termination_contact_indices, :] + self.terminated |= torch.any(torch.norm(contact_forces, dim=-1) > 1.0, dim=1) + self.timed_out = self.episode_length_buf >= self.max_episode_length + # self.to_be_reset = self.timed_out | self.terminated + + # --- + def _switch(self): c_vel = torch.linalg.norm(self.commands, dim=1) return torch.exp( diff --git a/gym/envs/mini_cheetah/mini_cheetah_ref_config.py b/gym/envs/mini_cheetah/mini_cheetah_ref_config.py index 1803c765..b8a7a2fe 100644 --- a/gym/envs/mini_cheetah/mini_cheetah_ref_config.py +++ b/gym/envs/mini_cheetah/mini_cheetah_ref_config.py @@ -26,7 +26,7 @@ class control(MiniCheetahCfg.control): stiffness = {"haa": 20.0, "hfe": 20.0, "kfe": 20.0} damping = {"haa": 0.5, "hfe": 0.5, "kfe": 0.5} gait_freq = 3.0 - ctrl_frequency = 100 + ctrl_frequency = 500 desired_sim_frequency = 500 class commands(MiniCheetahCfg.commands): @@ -70,6 +70,7 @@ class MiniCheetahRefRunnerCfg(MiniCheetahRunnerCfg): runner_class_name = "OnPolicyRunner" class actor(MiniCheetahRunnerCfg.actor): + frequency = 100 hidden_dims = [256, 256, 128] # * can be elu, relu, selu, crelu, lrelu, tanh, sigmoid activation = "elu" @@ -122,12 +123,12 @@ class weights: lin_vel_z = 0.0 ang_vel_xy = 0.01 orientation = 1.0 - torques = 5.0e-7 + torques = 5.0e-8 dof_vel = 0.0 min_base_height = 1.5 collision = 0.0 - action_rate = 0.1 - action_rate2 = 0.01 + action_rate = 1e-4 + action_rate2 = 1e-5 stand_still = 0.0 dof_pos_limits = 0.0 feet_contact_forces = 0.0 diff --git a/gym/envs/mit_humanoid/lander_config.py b/gym/envs/mit_humanoid/lander_config.py index 48c2eb53..86f3329f 100644 --- a/gym/envs/mit_humanoid/lander_config.py +++ b/gym/envs/mit_humanoid/lander_config.py @@ -282,7 +282,7 @@ class weights: power = 1e-6 # 1.0e-2 min_base_height = 1.5 lin_vel_xy = 1.0 - action_rate = 1e-3 + action_rate = 1e-2 action_rate2 = 1e-3 lin_vel_z = 0.0 ang_vel_xy = 0.0 diff --git a/gym/utils/task_registry.py b/gym/utils/task_registry.py index 6c2025ba..cc0787ca 100644 --- a/gym/utils/task_registry.py +++ b/gym/utils/task_registry.py @@ -178,6 +178,9 @@ def set_control_and_sim_dt(self, env_cfg, train_cfg): f" to {env_cfg.sim_dt}." ) + if not hasattr(train_cfg.actor, "frequency"): + train_cfg.actor.frequency = env_cfg.control.ctrl_frequency + def set_discount_rates(self, train_cfg, dt): if hasattr(train_cfg.algorithm, "discount_horizon"): hrzn = train_cfg.algorithm.discount_horizon diff --git a/learning/runners/my_runner.py b/learning/runners/my_runner.py index 15192d2d..4c2f5d52 100644 --- a/learning/runners/my_runner.py +++ b/learning/runners/my_runner.py @@ -39,8 +39,7 @@ def learn(self, states_to_log_dict=None): n_policy_steps = int((1 / self.env.dt) / self.actor_cfg["frequency"]) assert n_policy_steps > 0, "actor frequency should be less than ctrl_freq" - rewards_dict = {} - rewards_list = [dict() for x in range(n_policy_steps)] + rewards_dict = self.initialize_rewards_dict(n_policy_steps) self.alg.switch_to_train() actor_obs = self.get_obs(self.actor_cfg["obs"]) @@ -104,31 +103,21 @@ def learn(self, states_to_log_dict=None): for step in range(n_policy_steps): self.env.step() # put reward integration here - self.update_rewards(rewards_list[step], self.env.terminated) - # handle time-outs/terminations happening inside here with mask + self.update_rewards_dict(rewards_dict, step) else: # catch and reset failed envs to_be_reset = self.env.timed_out | self.env.terminated env_ids = (to_be_reset).nonzero(as_tuple=False).flatten() self.env._reset_idx(env_ids) + total_rewards = torch.stack( + tuple(rewards_dict.sum(dim=0).values()) + ).sum(dim=(0)) actor_obs = self.get_noisy_obs( self.actor_cfg["obs"], self.actor_cfg["noise"] ) critic_obs = self.get_obs(self.critic_cfg["obs"]) - # * get time_outs - - self.update_rewards(rewards_dict, self.env.terminated) - - # integrate rewards from list - total_rewards = torch.stack( - tuple( - torch.stack(tuple(rewards_dict.values())) - for rewards_dict in rewards_list - ) - ).sum(dim=(0, 1)) - transition.update( { "next_actor_obs": actor_obs, @@ -140,7 +129,7 @@ def learn(self, states_to_log_dict=None): ) storage.add_transitions(transition) - logger.log_rewards(rewards_dict) + logger.log_rewards(rewards_dict.sum(dim=0)) logger.log_rewards({"total_rewards": total_rewards}) logger.finish_step(self.env.timed_out | self.env.terminated) logger.toc("collection") @@ -205,6 +194,40 @@ def update_rewards(self, rewards_dict, terminated): ) ) + def initialize_rewards_dict(self, n_steps): + # sum existing rewards with new rewards + rewards_dict = TensorDict( + {}, batch_size=(n_steps, self.env.num_envs), device=self.device + ) + for key in self.critic_cfg["reward"]["termination_weight"]: + rewards_dict.update( + {key: torch.zeros(n_steps, self.env.num_envs, device=self.device)} + ) + for key in self.critic_cfg["reward"]["weights"]: + rewards_dict.update( + {key: torch.zeros(n_steps, self.env.num_envs, device=self.device)} + ) + return rewards_dict + + def update_rewards_dict(self, rewards_dict, step): + # sum existing rewards with new rewards + rewards_dict[step].update( + self.get_rewards( + self.critic_cfg["reward"]["termination_weight"], + modifier=self.env.dt, + mask=self.env.terminated, + ), + inplace=True, + ) + rewards_dict[step].update( + self.get_rewards( + self.critic_cfg["reward"]["weights"], + modifier=self.env.dt, + mask=~self.env.terminated, + ), + inplace=True, + ) + def sim_and_log_states(self, states_to_log_dict, it_idx): # Simulate environment for as many steps as expected in the dict. # Log states to the dict, as well as whether the env terminated. diff --git a/learning/runners/off_policy_runner.py b/learning/runners/off_policy_runner.py index 7aed58e7..516c4a3e 100644 --- a/learning/runners/off_policy_runner.py +++ b/learning/runners/off_policy_runner.py @@ -48,7 +48,10 @@ def _set_up_alg(self): def learn(self): self.set_up_logger() - rewards_dict = {} + n_policy_steps = int((1 / self.env.dt) / self.actor_cfg["frequency"]) + assert n_policy_steps > 0, "actor frequency should be less than ctrl_freq" + + rewards_dict = self.initialize_rewards_dict(n_policy_steps) self.alg.switch_to_train() actor_obs = self.get_obs(self.actor_cfg["obs"]) @@ -94,28 +97,32 @@ def learn(self): } ) - self.env.step() + for step in range(n_policy_steps): + self.env.step() + # put reward integration here + self.update_rewards_dict(rewards_dict, step) + else: + # catch and reset failed envs + to_be_reset = self.env.timed_out | self.env.terminated + env_ids = (to_be_reset).nonzero(as_tuple=False).flatten() + self.env._reset_idx(env_ids) + + total_rewards = torch.stack( + tuple(rewards_dict.sum(dim=0).values()) + ).sum(dim=(0)) actor_obs = self.get_noisy_obs( self.actor_cfg["obs"], self.actor_cfg["noise"] ) critic_obs = self.get_obs(self.critic_cfg["obs"]) - # * get time_outs - timed_out = self.get_timed_out() - terminated = self.get_terminated() - dones = timed_out | terminated - - self.update_rewards(rewards_dict, terminated) - total_rewards = torch.stack(tuple(rewards_dict.values())).sum(dim=0) - transition.update( { "next_actor_obs": actor_obs, "next_critic_obs": critic_obs, "rewards": total_rewards, - "timed_out": timed_out, - "dones": dones, + "timed_out": self.env.timed_out, + "dones": self.env.timed_out | self.env.terminated, } ) storage.add_transitions(transition) @@ -147,35 +154,39 @@ def learn(self): } ) - self.env.step() + for step in range(n_policy_steps): + self.env.step() + # put reward integration here + self.update_rewards_dict(rewards_dict, step) + else: + # catch and reset failed envs + to_be_reset = self.env.timed_out | self.env.terminated + env_ids = (to_be_reset).nonzero(as_tuple=False).flatten() + self.env._reset_idx(env_ids) + + total_rewards = torch.stack( + tuple(rewards_dict.sum(dim=0).values()) + ).sum(dim=(0)) actor_obs = self.get_noisy_obs( self.actor_cfg["obs"], self.actor_cfg["noise"] ) critic_obs = self.get_obs(self.critic_cfg["obs"]) - # * get time_outs - timed_out = self.get_timed_out() - terminated = self.get_terminated() - dones = timed_out | terminated - - self.update_rewards(rewards_dict, terminated) - total_rewards = torch.stack(tuple(rewards_dict.values())).sum(dim=0) - transition.update( { "next_actor_obs": actor_obs, "next_critic_obs": critic_obs, "rewards": total_rewards, - "timed_out": timed_out, - "dones": dones, + "timed_out": self.env.timed_out, + "dones": self.env.timed_out | self.env.terminated, } ) storage.add_transitions(transition) - logger.log_rewards(rewards_dict) + logger.log_rewards(rewards_dict.sum(dim=0)) logger.log_rewards({"total_rewards": total_rewards}) - logger.finish_step(dones) + logger.finish_step(self.env.timed_out | self.env.terminated) logger.toc("collection") logger.tic("learning") @@ -192,20 +203,40 @@ def learn(self): self.save() self.save() - def update_rewards(self, rewards_dict, terminated): - rewards_dict.update( + def update_rewards_dict(self, rewards_dict, step): + # sum existing rewards with new rewards + rewards_dict[step].update( self.get_rewards( - self.critic_cfg["reward"]["termination_weight"], mask=terminated - ) + self.critic_cfg["reward"]["termination_weight"], + modifier=self.env.dt, + mask=self.env.terminated, + ), + inplace=True, ) - rewards_dict.update( + rewards_dict[step].update( self.get_rewards( self.critic_cfg["reward"]["weights"], modifier=self.env.dt, - mask=~terminated, - ) + mask=~self.env.terminated, + ), + inplace=True, ) + def initialize_rewards_dict(self, n_steps): + # sum existing rewards with new rewards + rewards_dict = TensorDict( + {}, batch_size=(n_steps, self.env.num_envs), device=self.device + ) + for key in self.critic_cfg["reward"]["termination_weight"]: + rewards_dict.update( + {key: torch.zeros(n_steps, self.env.num_envs, device=self.device)} + ) + for key in self.critic_cfg["reward"]["weights"]: + rewards_dict.update( + {key: torch.zeros(n_steps, self.env.num_envs, device=self.device)} + ) + return rewards_dict + def set_up_logger(self): logger.register_rewards(list(self.critic_cfg["reward"]["weights"].keys())) logger.register_rewards( diff --git a/learning/runners/on_policy_runner.py b/learning/runners/on_policy_runner.py index 0a81680d..7167f03e 100644 --- a/learning/runners/on_policy_runner.py +++ b/learning/runners/on_policy_runner.py @@ -24,7 +24,10 @@ def __init__(self, env, train_cfg, device="cpu"): def learn(self, states_to_log_dict=None): self.set_up_logger() - rewards_dict = {} + n_policy_steps = int((1 / self.env.dt) / self.actor_cfg["frequency"]) + assert n_policy_steps > 0, "actor frequency should be less than ctrl_freq" + + rewards_dict = self.initialize_rewards_dict(n_policy_steps) self.alg.switch_to_train() actor_obs = self.get_obs(self.actor_cfg["obs"]) @@ -86,37 +89,39 @@ def learn(self, states_to_log_dict=None): "critic_obs": critic_obs, } ) + for step in range(n_policy_steps): + self.env.step() + # put reward integration here + self.update_rewards_dict(rewards_dict, step) + else: + # catch and reset failed envs + to_be_reset = self.env.timed_out | self.env.terminated + env_ids = (to_be_reset).nonzero(as_tuple=False).flatten() + self.env._reset_idx(env_ids) - self.env.step() + total_rewards = torch.stack( + tuple(rewards_dict.sum(dim=0).values()) + ).sum(dim=(0)) actor_obs = self.get_noisy_obs( self.actor_cfg["obs"], self.actor_cfg["noise"] ) critic_obs = self.get_obs(self.critic_cfg["obs"]) - # * get time_outs - timed_out = self.get_timed_out() - terminated = self.get_terminated() - dones = timed_out | terminated - - self.update_rewards(rewards_dict, terminated) - total_rewards = torch.stack(tuple(rewards_dict.values())).sum(dim=0) - transition.update( { "next_actor_obs": actor_obs, "next_critic_obs": critic_obs, "rewards": total_rewards, - "timed_out": timed_out, - "terminated": terminated, - "dones": dones, + "timed_out": self.env.timed_out, + "dones": self.env.timed_out | self.env.terminated, } ) storage.add_transitions(transition) - logger.log_rewards(rewards_dict) + logger.log_rewards(rewards_dict.sum(dim=0)) logger.log_rewards({"total_rewards": total_rewards}) - logger.finish_step(dones) + logger.finish_step(self.env.timed_out | self.env.terminated) logger.toc("collection") logger.tic("learning") @@ -149,19 +154,39 @@ def burn_in_normalization(self, n_iterations=100): self.alg.critic.evaluate(critic_obs) self.env.reset() - def update_rewards(self, rewards_dict, terminated): - rewards_dict.update( + def update_rewards_dict(self, rewards_dict, step): + # sum existing rewards with new rewards + rewards_dict[step].update( self.get_rewards( - self.critic_cfg["reward"]["termination_weight"], mask=terminated - ) + self.critic_cfg["reward"]["termination_weight"], + modifier=self.env.dt, + mask=self.env.terminated, + ), + inplace=True, ) - rewards_dict.update( + rewards_dict[step].update( self.get_rewards( self.critic_cfg["reward"]["weights"], modifier=self.env.dt, - mask=~terminated, - ) + mask=~self.env.terminated, + ), + inplace=True, + ) + + def initialize_rewards_dict(self, n_steps): + # sum existing rewards with new rewards + rewards_dict = TensorDict( + {}, batch_size=(n_steps, self.env.num_envs), device=self.device ) + for key in self.critic_cfg["reward"]["termination_weight"]: + rewards_dict.update( + {key: torch.zeros(n_steps, self.env.num_envs, device=self.device)} + ) + for key in self.critic_cfg["reward"]["weights"]: + rewards_dict.update( + {key: torch.zeros(n_steps, self.env.num_envs, device=self.device)} + ) + return rewards_dict def set_up_logger(self): logger.register_rewards(list(self.critic_cfg["reward"]["weights"].keys())) From e3956738f7ae709874a889b7868abd5fc38dd3f3 Mon Sep 17 00:00:00 2001 From: Steve Heim Date: Mon, 9 Sep 2024 14:23:38 -0400 Subject: [PATCH 31/77] refactor skeleton with a super_init fix, and pre-initialize reward buffer. Doesn't seem to make a difference in speed. --- gym/envs/base/base_task.py | 19 +++---------------- gym/envs/base/task_skeleton.py | 19 +++++++++++-------- 2 files changed, 14 insertions(+), 24 deletions(-) diff --git a/gym/envs/base/base_task.py b/gym/envs/base/base_task.py index 551143a3..033e0eeb 100644 --- a/gym/envs/base/base_task.py +++ b/gym/envs/base/base_task.py @@ -18,33 +18,20 @@ def __init__(self, gym, sim, cfg, sim_params, sim_device, headless): # * env device is GPU only if sim is on GPU and use_gpu_pipeline=True, # * otherwise returned tensors are copied to CPU by physX. if sim_device_type == "cuda" and sim_params.use_gpu_pipeline: - self.device = self.sim_device + device = self.sim_device else: - self.device = "cpu" + device = "cpu" # * graphics device for rendering, -1 for no rendering self.graphics_device_id = self.sim_device_id - self.num_envs = cfg.env.num_envs self.num_actuators = cfg.env.num_actuators # * optimization flags for pytorch JIT torch._C._jit_set_profiling_mode(False) torch._C._jit_set_profiling_executor(False) - # allocate buffers - self.to_be_reset = torch.ones( - self.num_envs, device=self.device, dtype=torch.bool - ) - self.terminated = torch.ones( - self.num_envs, device=self.device, dtype=torch.bool - ) - self.episode_length_buf = torch.zeros( - self.num_envs, device=self.device, dtype=torch.long - ) - self.timed_out = torch.zeros( - self.num_envs, device=self.device, dtype=torch.bool - ) + super().__init__(num_envs=cfg.env.num_envs, device=device) # todo: read from config self.enable_viewer_sync = True diff --git a/gym/envs/base/task_skeleton.py b/gym/envs/base/task_skeleton.py index 4f931eab..d3585e45 100644 --- a/gym/envs/base/task_skeleton.py +++ b/gym/envs/base/task_skeleton.py @@ -5,13 +5,15 @@ class TaskSkeleton: - def __init__( - self, num_envs=1, max_episode_length=1.0, device="cpu", auto_reset=True - ): + def __init__(self, num_envs=1, device="cpu"): self.num_envs = num_envs - self.max_episode_length = max_episode_length self.device = device - self.auto_reset = auto_reset + + self.to_be_reset = torch.ones(num_envs, device=device, dtype=torch.bool) + self.terminated = torch.ones(num_envs, device=device, dtype=torch.bool) + self.episode_length_buf = torch.zeros(num_envs, device=device, dtype=torch.long) + self.timed_out = torch.zeros(num_envs, device=device, dtype=torch.bool) + self.reward_buf = torch.zeros(num_envs, device=device, dtype=torch.float) return None @@ -61,10 +63,11 @@ def compute_reward(self, reward_weights): reward_weights: dict with keys matching reward names, and values matching weights """ - reward = torch.zeros(self.num_envs, device=self.device, dtype=torch.float) + # reward = torch.zeros(self.num_envs, device=self.device, dtype=torch.float) + self.reward_buf[:] = 0 for name, weight in reward_weights.items(): - reward += weight * self._eval_reward(name) - return reward + self.reward_buf += weight * self._eval_reward(name) + return self.reward_buf def _eval_reward(self, name): return eval("self._reward_" + name + "()") From ea5cdff304ef6edc78dbd66e4ecfcd9d7d4cccb1 Mon Sep 17 00:00:00 2001 From: Steve Heim Date: Mon, 9 Sep 2024 15:09:09 -0400 Subject: [PATCH 32/77] refactor: redo rewards computation, with a dict of reward functions in the runner. Seems to make a tiny difference in speed. More importantly, it removes the `eval` hack, which means we can profile individual reward functions. THIS BREAKS PBRS --- gym/envs/base/task_skeleton.py | 15 --------------- learning/runners/BaseRunner.py | 15 +++++++++++---- 2 files changed, 11 insertions(+), 19 deletions(-) diff --git a/gym/envs/base/task_skeleton.py b/gym/envs/base/task_skeleton.py index d3585e45..4dfcb280 100644 --- a/gym/envs/base/task_skeleton.py +++ b/gym/envs/base/task_skeleton.py @@ -13,7 +13,6 @@ def __init__(self, num_envs=1, device="cpu"): self.terminated = torch.ones(num_envs, device=device, dtype=torch.bool) self.episode_length_buf = torch.zeros(num_envs, device=device, dtype=torch.long) self.timed_out = torch.zeros(num_envs, device=device, dtype=torch.bool) - self.reward_buf = torch.zeros(num_envs, device=device, dtype=torch.float) return None @@ -58,20 +57,6 @@ def _reset_buffers(self): self.terminated[:] = False self.timed_out[:] = False - def compute_reward(self, reward_weights): - """Compute and return a torch tensor of rewards - reward_weights: dict with keys matching reward names, and values - matching weights - """ - # reward = torch.zeros(self.num_envs, device=self.device, dtype=torch.float) - self.reward_buf[:] = 0 - for name, weight in reward_weights.items(): - self.reward_buf += weight * self._eval_reward(name) - return self.reward_buf - - def _eval_reward(self, name): - return eval("self._reward_" + name + "()") - def _check_terminations_and_timeouts(self): """Check if environments need to be reset""" contact_forces = self.contact_forces[:, self.termination_contact_indices, :] diff --git a/learning/runners/BaseRunner.py b/learning/runners/BaseRunner.py index 01a1b825..1545293b 100644 --- a/learning/runners/BaseRunner.py +++ b/learning/runners/BaseRunner.py @@ -8,8 +8,8 @@ class BaseRunner: def __init__(self, env, train_cfg, device="cpu"): self.device = device self.env = env + self.setup_reward_functions() self.parse_train_cfg(train_cfg) - self.num_steps_per_env = self.cfg["num_steps_per_env"] self.save_interval = self.cfg["save_interval"] self.num_learning_iterations = self.cfg["max_iterations"] @@ -18,6 +18,13 @@ def __init__(self, env, train_cfg, device="cpu"): self.log_dir = train_cfg["log_dir"] self._set_up_alg() + def setup_reward_functions(self): + self.reward_functions = { + method.replace("_reward_", ""): getattr(self.env, method) + for method in dir(self.env) + if callable(getattr(self.env, method)) and method.startswith("_reward_") + } + def _set_up_alg(self): num_actor_obs = self.get_obs_size(self.actor_cfg["obs"]) num_actions = self.get_action_size(self.actor_cfg["actions"]) @@ -95,8 +102,8 @@ def get_rewards(self, reward_weights, modifier=1, mask=None): if mask is None: mask = 1.0 for name, weight in reward_weights.items(): - rewards_dict[name] = mask * self._get_reward({name: weight}, modifier) + rewards_dict[name] = mask * self._get_reward(name, weight * modifier) return rewards_dict - def _get_reward(self, name_weight, modifier=1): - return modifier * self.env.compute_reward(name_weight).to(self.device) + def _get_reward(self, name, weight): + return weight * self.reward_functions[name]().to(self.device) From 7296c6c48903a5458c2e3d4e2c50e72bf430d97a Mon Sep 17 00:00:00 2001 From: Steve Heim Date: Mon, 9 Sep 2024 15:58:57 -0400 Subject: [PATCH 33/77] compute switch once per decimation (speedup ~10%) --- gym/envs/mini_cheetah/mini_cheetah_osc.py | 20 +++++++++++++------ gym/envs/mini_cheetah/mini_cheetah_ref.py | 19 ++++++++++-------- .../mini_cheetah/mini_cheetah_ref_config.py | 1 + 3 files changed, 26 insertions(+), 14 deletions(-) diff --git a/gym/envs/mini_cheetah/mini_cheetah_osc.py b/gym/envs/mini_cheetah/mini_cheetah_osc.py index acec0b8a..6d44e35d 100644 --- a/gym/envs/mini_cheetah/mini_cheetah_osc.py +++ b/gym/envs/mini_cheetah/mini_cheetah_osc.py @@ -16,6 +16,9 @@ def __init__(self, gym, sim, cfg, sim_params, sim_device, headless): def _init_buffers(self): super()._init_buffers() + + self._switch = torch.zeros(self.num_envs, 1, device=self.device) + self.oscillators = torch.zeros(self.num_envs, 4, device=self.device) self.oscillator_obs = torch.zeros(self.num_envs, 8, device=self.device) @@ -158,6 +161,7 @@ def _post_decimation_step(self): """Update all states that are not handled in PhysX""" super()._post_decimation_step() self.grf = self._compute_grf() + self._update_cmd_switch() # self._step_oscillators() def _post_physx_step(self): @@ -266,12 +270,16 @@ def _compute_grf(self, grf_norm=True): else: return grf - def _switch(self): + def _update_cmd_switch(self): c_vel = torch.linalg.norm(self.commands, dim=1) - return torch.exp( + self._switch = torch.exp( -torch.square(torch.max(torch.zeros_like(c_vel), c_vel - 0.1)) / self.cfg.reward_settings.switch_scale ) + # return torch.exp( + # -torch.square(torch.max(torch.zeros_like(c_vel), c_vel - 0.1)) + # / self.cfg.reward_settings.switch_scale + # ) def _reward_cursorial(self): # penalize the abad joints being away from 0 @@ -357,10 +365,10 @@ def _reward_stance_velocity(self): return -torch.mean(rew, dim=1) def _reward_dof_vel(self): - return super()._reward_dof_vel() * self._switch() + return super()._reward_dof_vel() * self._switch def _reward_dof_near_home(self): - return super()._reward_dof_near_home() * self._switch() + return super()._reward_dof_near_home() * self._switch def _reward_stand_still(self): """Penalize motion at zero commands""" @@ -371,11 +379,11 @@ def _reward_stand_still(self): rew_vel = torch.mean(self._sqrdexp(self.dof_vel), dim=1) rew_base_vel = torch.mean(torch.square(self.base_lin_vel), dim=1) rew_base_vel += torch.mean(torch.square(self.base_ang_vel), dim=1) - return (rew_vel + rew_pos - rew_base_vel) * self._switch() + return (rew_vel + rew_pos - rew_base_vel) * self._switch def _reward_standing_torques(self): """Penalize torques at zero commands""" - return super()._reward_torques() * self._switch() + return super()._reward_torques() * self._switch # * gait similarity scores def angle_difference(self, theta1, theta2): diff --git a/gym/envs/mini_cheetah/mini_cheetah_ref.py b/gym/envs/mini_cheetah/mini_cheetah_ref.py index d3f8a89b..0084a80c 100644 --- a/gym/envs/mini_cheetah/mini_cheetah_ref.py +++ b/gym/envs/mini_cheetah/mini_cheetah_ref.py @@ -17,6 +17,7 @@ def __init__(self, gym, sim, cfg, sim_params, sim_device, headless): def _init_buffers(self): super()._init_buffers() + self._switch = torch.zeros(self.num_envs, 1, device=self.device) self.phase = torch.zeros( self.num_envs, 1, dtype=torch.float, device=self.device ) @@ -41,6 +42,7 @@ def _post_decimation_step(self): self.phase_obs = torch.cat( (torch.sin(self.phase), torch.cos(self.phase)), dim=1 ) + self._update_cmd_switch() def _resample_commands(self, env_ids): super()._resample_commands(env_ids) @@ -59,10 +61,11 @@ def _check_terminations_and_timeouts(self): # --- - def _switch(self): + def _update_cmd_switch(self): c_vel = torch.linalg.norm(self.commands, dim=1) - return torch.exp( - -torch.square(torch.max(torch.zeros_like(c_vel), c_vel - 0.1)) / 0.1 + self._switch = torch.exp( + -torch.square(torch.max(torch.zeros_like(c_vel), c_vel - 0.1)) + / self.cfg.reward_settings.switch_scale ) def _reward_swing_grf(self): @@ -73,7 +76,7 @@ def _reward_swing_grf(self): ) ph_off = torch.lt(self.phase, torch.pi) rew = in_contact * torch.cat((ph_off, ~ph_off, ~ph_off, ph_off), dim=1) - return -torch.sum(rew.float(), dim=1) * (1 - self._switch()) + return -torch.sum(rew.float(), dim=1) * (1 - self._switch) def _reward_stance_grf(self): """Reward non-zero grf during stance (pi to 2pi)""" @@ -84,7 +87,7 @@ def _reward_stance_grf(self): ph_off = torch.gt(self.phase, torch.pi) # should this be in swing? rew = in_contact * torch.cat((ph_off, ~ph_off, ~ph_off, ph_off), dim=1) - return torch.sum(rew.float(), dim=1) * (1 - self._switch()) + return torch.sum(rew.float(), dim=1) * (1 - self._switch) def _reward_reference_traj(self): """REWARDS EACH LEG INDIVIDUALLY BASED ON ITS POSITION IN THE CYCLE""" @@ -93,7 +96,7 @@ def _reward_reference_traj(self): error /= self.scales["dof_pos"] reward = (self._sqrdexp(error) - torch.abs(error) * 0.2).mean(dim=1) # * only when commanded velocity is higher - return reward * (1 - self._switch()) + return reward * (1 - self._switch) def _get_ref(self): leg_frame = torch.zeros_like(self.torques) @@ -121,10 +124,10 @@ def _reward_stand_still(self): rew_vel = torch.mean(self._sqrdexp(self.dof_vel), dim=1) rew_base_vel = torch.mean(torch.square(self.base_lin_vel), dim=1) rew_base_vel += torch.mean(torch.square(self.base_ang_vel), dim=1) - return (rew_vel + rew_pos - rew_base_vel) * self._switch() + return (rew_vel + rew_pos - rew_base_vel) * self._switch def _reward_tracking_lin_vel(self): """Tracking linear velocity commands (xy axes)""" # just use lin_vel? reward = super()._reward_tracking_lin_vel() - return reward * (1 - self._switch()) + return reward * (1 - self._switch) diff --git a/gym/envs/mini_cheetah/mini_cheetah_ref_config.py b/gym/envs/mini_cheetah/mini_cheetah_ref_config.py index b8a7a2fe..7a54bb1d 100644 --- a/gym/envs/mini_cheetah/mini_cheetah_ref_config.py +++ b/gym/envs/mini_cheetah/mini_cheetah_ref_config.py @@ -60,6 +60,7 @@ class reward_settings(MiniCheetahCfg.reward_settings): max_contact_force = 600.0 base_height_target = 0.3 tracking_sigma = 0.25 + switch_scale = 0.1 class scaling(MiniCheetahCfg.scaling): pass From 69218dd1a7ffcd1c8a898a160f1bb6f17a75e888 Mon Sep 17 00:00:00 2001 From: Steve Heim Date: Tue, 10 Sep 2024 17:55:53 -0400 Subject: [PATCH 34/77] fixed logging bug that scaled with wrong dt --- gym/envs/base/fixed_robot.py | 21 ++++-------- gym/envs/base/legged_robot.py | 20 ++++------- gym/envs/base/legged_robot_config.py | 3 -- .../mini_cheetah/mini_cheetah_ref_config.py | 33 ++++++++++++++----- gym/envs/pendulum/pendulum_SAC_config.py | 3 +- gym/envs/pendulum/pendulum_config.py | 1 + learning/runners/my_runner.py | 17 +++++----- learning/runners/off_policy_runner.py | 17 +++++----- learning/runners/on_policy_runner.py | 17 +++++----- 9 files changed, 64 insertions(+), 68 deletions(-) diff --git a/gym/envs/base/fixed_robot.py b/gym/envs/base/fixed_robot.py index 544eba52..f623e5d1 100644 --- a/gym/envs/base/fixed_robot.py +++ b/gym/envs/base/fixed_robot.py @@ -580,27 +580,18 @@ def _reward_dof_vel(self): def _reward_action_rate(self): """Penalize changes in actions""" nact = self.num_actuators - dt2 = (self.dt * self.cfg.control.decimation) ** 2 - error = ( - torch.square( - self.dof_pos_history[:, :nact] - - self.dof_pos_history[:, nact : 2 * nact] - ) - / dt2 + error = torch.square( + self.dof_pos_history[:, :nact] - self.dof_pos_history[:, 2 * nact :] ) return -torch.mean(error, dim=1) def _reward_action_rate2(self): """Penalize changes in actions""" nact = self.num_actuators - dt2 = (self.dt * self.cfg.control.decimation) ** 2 - error = ( - torch.square( - self.dof_pos_history[:, :nact] - - 2 * self.dof_pos_history[:, nact : 2 * nact] - + self.dof_pos_history[:, 2 * nact :] - ) - / dt2 + error = torch.square( + self.dof_pos_history[:, :nact] + - 2 * self.dof_pos_history[:, nact : 2 * nact] + + self.dof_pos_history[:, 2 * nact :] ) return -torch.mean(error, dim=1) diff --git a/gym/envs/base/legged_robot.py b/gym/envs/base/legged_robot.py index 4433d4fb..c80ebc85 100644 --- a/gym/envs/base/legged_robot.py +++ b/gym/envs/base/legged_robot.py @@ -1030,26 +1030,18 @@ def _reward_dof_vel(self): def _reward_action_rate(self): """Penalize changes in actions""" n = self.num_actuators - dt2 = (self.dt * self.cfg.control.decimation) ** 2 - error = ( - torch.square( - self.dof_pos_history[:, :n] - self.dof_pos_history[:, n : 2 * n] - ) - / dt2 + error = torch.square( + self.dof_pos_history[:, :n] - self.dof_pos_history[:, 2 * n :] ) return -torch.mean(error, dim=1) def _reward_action_rate2(self): """Penalize changes in actions""" n = self.num_actuators - dt2 = (self.dt * self.cfg.control.decimation) ** 2 - error = ( - torch.square( - self.dof_pos_history[:, :n] - - 2 * self.dof_pos_history[:, n : 2 * n] - + self.dof_pos_history[:, 2 * n :] - ) - / dt2 + error = torch.square( + self.dof_pos_history[:, :n] + - 2 * self.dof_pos_history[:, n : 2 * n] + + self.dof_pos_history[:, 2 * n :] ) return -torch.mean(error, dim=1) diff --git a/gym/envs/base/legged_robot_config.py b/gym/envs/base/legged_robot_config.py index 4336a749..c38f3e33 100644 --- a/gym/envs/base/legged_robot_config.py +++ b/gym/envs/base/legged_robot_config.py @@ -301,9 +301,6 @@ class algorithm: batch_size = 2**15 max_gradient_steps = 24 # new - storage_size = 2**17 # new - batch_size = 2**15 # new - clip_param = 0.2 learning_rate = 1.0e-3 max_grad_norm = 1.0 diff --git a/gym/envs/mini_cheetah/mini_cheetah_ref_config.py b/gym/envs/mini_cheetah/mini_cheetah_ref_config.py index 7a54bb1d..d3a301d3 100644 --- a/gym/envs/mini_cheetah/mini_cheetah_ref_config.py +++ b/gym/envs/mini_cheetah/mini_cheetah_ref_config.py @@ -85,7 +85,7 @@ class actor(MiniCheetahRunnerCfg.actor): "dof_vel", "phase_obs", ] - normalize_obs = True + normalize_obs = False actions = ["dof_pos_target"] disable_actions = False @@ -115,7 +115,7 @@ class critic(MiniCheetahRunnerCfg.critic): "phase_obs", "dof_pos_target", ] - normalize_obs = True + normalize_obs = False class reward: class weights: @@ -128,13 +128,13 @@ class weights: dof_vel = 0.0 min_base_height = 1.5 collision = 0.0 - action_rate = 1e-4 - action_rate2 = 1e-5 + action_rate = 1e-5 + action_rate2 = 1e-6 stand_still = 0.0 dof_pos_limits = 0.0 feet_contact_forces = 0.0 dof_near_home = 0.0 - reference_traj = 1.5 + reference_traj = 0.0 swing_grf = 1.5 stance_grf = 1.5 @@ -142,11 +142,28 @@ class termination_weight: termination = 0.15 class algorithm(MiniCheetahRunnerCfg.algorithm): - pass + # both + gamma = 0.99 + lam = 0.95 + # shared + batch_size = 2 * 4096 # use all the data + max_gradient_steps = 50 + + clip_param = 0.2 + learning_rate = 1.0e-3 + max_grad_norm = 1.0 + # Critic + use_clipped_value_loss = True + # Actor + entropy_coef = 0.01 + schedule = "adaptive" # could be adaptive, fixed + desired_kl = 0.01 + lr_range = [2e-5, 1e-2] + lr_ratio = 1.5 class runner(MiniCheetahRunnerCfg.runner): run_name = "" experiment_name = "mini_cheetah_ref" - max_iterations = 1000 # number of policy updates + max_iterations = 500 # number of policy updates algorithm_class_name = "PPO2" - num_steps_per_env = 32 # deprecate + num_steps_per_env = 20 # deprecate diff --git a/gym/envs/pendulum/pendulum_SAC_config.py b/gym/envs/pendulum/pendulum_SAC_config.py index 836a6185..42bf0265 100644 --- a/gym/envs/pendulum/pendulum_SAC_config.py +++ b/gym/envs/pendulum/pendulum_SAC_config.py @@ -17,7 +17,7 @@ class init_state(PendulumCfg.init_state): dof_vel_range = {"theta": [-5, 5]} class control(PendulumCfg.control): - ctrl_frequency = 10 + ctrl_frequency = 100 desired_sim_frequency = 100 class asset(PendulumCfg.asset): @@ -37,6 +37,7 @@ class PendulumSACRunnerCfg(FixedRobotCfgPPO): runner_class_name = "OffPolicyRunner" class actor(FixedRobotCfgPPO.actor): + frequency = 10 latent_nn = {"hidden_dims": [128, 64], "activation": "elu", "layer_norm": True} mean_nn = {"hidden_dims": [32], "activation": "elu", "layer_norm": True} std_nn = {"hidden_dims": [32], "activation": "elu", "layer_norm": True} diff --git a/gym/envs/pendulum/pendulum_config.py b/gym/envs/pendulum/pendulum_config.py index bb33c1f1..910fe341 100644 --- a/gym/envs/pendulum/pendulum_config.py +++ b/gym/envs/pendulum/pendulum_config.py @@ -62,6 +62,7 @@ class PendulumRunnerCfg(FixedRobotCfgPPO): runner_class_name = "OnPolicyRunner" class actor(FixedRobotCfgPPO.actor): + frequency = 25 hidden_dims = [128, 64, 32] # * can be elu, relu, selu, crelu, lrelu, tanh, sigmoid activation = "tanh" diff --git a/learning/runners/my_runner.py b/learning/runners/my_runner.py index 4c2f5d52..e0da1a1b 100644 --- a/learning/runners/my_runner.py +++ b/learning/runners/my_runner.py @@ -16,12 +16,6 @@ class MyRunner(OnPolicyRunner): def __init__(self, env, train_cfg, device="cpu"): super().__init__(env, train_cfg, device) - logger.initialize( - self.env.num_envs, - self.env.dt, - self.cfg["max_iterations"], - self.device, - ) def _set_up_alg(self): num_actor_obs = self.get_obs_size(self.actor_cfg["obs"]) @@ -34,10 +28,9 @@ def _set_up_alg(self): self.alg = alg_class(actor, critic, device=self.device, **self.alg_cfg) def learn(self, states_to_log_dict=None): - self.set_up_logger() - n_policy_steps = int((1 / self.env.dt) / self.actor_cfg["frequency"]) assert n_policy_steps > 0, "actor frequency should be less than ctrl_freq" + self.set_up_logger(dt=self.env.dt * n_policy_steps) rewards_dict = self.initialize_rewards_dict(n_policy_steps) @@ -164,7 +157,13 @@ def burn_in_normalization(self, n_iterations=100): self.alg.critic.evaluate(critic_obs) self.env.reset() - def set_up_logger(self): + def set_up_logger(self, dt=None): + if dt is None: + dt = self.env.dt + logger.initialize( + self.env.num_envs, dt, self.cfg["max_iterations"], self.device + ) + logger.register_rewards(list(self.critic_cfg["reward"]["weights"].keys())) logger.register_rewards( list(self.critic_cfg["reward"]["termination_weight"].keys()) diff --git a/learning/runners/off_policy_runner.py b/learning/runners/off_policy_runner.py index 516c4a3e..83249aef 100644 --- a/learning/runners/off_policy_runner.py +++ b/learning/runners/off_policy_runner.py @@ -16,12 +16,6 @@ class OffPolicyRunner(BaseRunner): def __init__(self, env, train_cfg, device="cpu"): super().__init__(env, train_cfg, device) - logger.initialize( - self.env.num_envs, - self.env.dt, - self.cfg["max_iterations"], - self.device, - ) def _set_up_alg(self): num_actor_obs = self.get_obs_size(self.actor_cfg["obs"]) @@ -46,10 +40,9 @@ def _set_up_alg(self): ) def learn(self): - self.set_up_logger() - n_policy_steps = int((1 / self.env.dt) / self.actor_cfg["frequency"]) assert n_policy_steps > 0, "actor frequency should be less than ctrl_freq" + self.set_up_logger(dt=self.env.dt * n_policy_steps) rewards_dict = self.initialize_rewards_dict(n_policy_steps) @@ -237,7 +230,13 @@ def initialize_rewards_dict(self, n_steps): ) return rewards_dict - def set_up_logger(self): + def set_up_logger(self, dt=None): + if dt is None: + dt = self.env.dt + logger.initialize( + self.env.num_envs, dt, self.cfg["max_iterations"], self.device + ) + logger.register_rewards(list(self.critic_cfg["reward"]["weights"].keys())) logger.register_rewards( list(self.critic_cfg["reward"]["termination_weight"].keys()) diff --git a/learning/runners/on_policy_runner.py b/learning/runners/on_policy_runner.py index 7167f03e..4831b615 100644 --- a/learning/runners/on_policy_runner.py +++ b/learning/runners/on_policy_runner.py @@ -14,18 +14,11 @@ class OnPolicyRunner(BaseRunner): def __init__(self, env, train_cfg, device="cpu"): super().__init__(env, train_cfg, device) - logger.initialize( - self.env.num_envs, - self.env.dt, - self.cfg["max_iterations"], - self.device, - ) def learn(self, states_to_log_dict=None): - self.set_up_logger() - n_policy_steps = int((1 / self.env.dt) / self.actor_cfg["frequency"]) assert n_policy_steps > 0, "actor frequency should be less than ctrl_freq" + self.set_up_logger(dt=self.env.dt * n_policy_steps) rewards_dict = self.initialize_rewards_dict(n_policy_steps) @@ -188,7 +181,13 @@ def initialize_rewards_dict(self, n_steps): ) return rewards_dict - def set_up_logger(self): + def set_up_logger(self, dt=None): + if dt is None: + dt = self.env.dt + logger.initialize( + self.env.num_envs, dt, self.cfg["max_iterations"], self.device + ) + logger.register_rewards(list(self.critic_cfg["reward"]["weights"].keys())) logger.register_rewards( list(self.critic_cfg["reward"]["termination_weight"].keys()) From b1bd4f2b9b20c2c67877d61e8c4daee513d65b17 Mon Sep 17 00:00:00 2001 From: Steve Heim Date: Wed, 11 Sep 2024 14:37:52 -0400 Subject: [PATCH 35/77] hardcode Jacobian (halves time for _apply_coupling) --- .../mini_cheetah/mini_cheetah_ref_config.py | 4 ++ gym/envs/mit_humanoid/mit_humanoid.py | 28 ++++++------ gym/envs/mit_humanoid/mit_humanoid_config.py | 43 +++++++++---------- learning/modules/QRCritics.py | 2 +- 4 files changed, 39 insertions(+), 38 deletions(-) diff --git a/gym/envs/mini_cheetah/mini_cheetah_ref_config.py b/gym/envs/mini_cheetah/mini_cheetah_ref_config.py index d3a301d3..37c52919 100644 --- a/gym/envs/mini_cheetah/mini_cheetah_ref_config.py +++ b/gym/envs/mini_cheetah/mini_cheetah_ref_config.py @@ -74,6 +74,7 @@ class actor(MiniCheetahRunnerCfg.actor): frequency = 100 hidden_dims = [256, 256, 128] # * can be elu, relu, selu, crelu, lrelu, tanh, sigmoid + layer_norm = [True, True, False] activation = "elu" smooth_exploration = False exploration_sample_freq = 16 @@ -102,6 +103,9 @@ class noise: class critic(MiniCheetahRunnerCfg.critic): hidden_dims = [256, 256, 128] + layer_norm = [True, True, False] + dropouts = [0.1, 0.0, 0.0] + # * can be elu, relu, selu, crelu, lrelu, tanh, sigmoid activation = "elu" obs = [ diff --git a/gym/envs/mit_humanoid/mit_humanoid.py b/gym/envs/mit_humanoid/mit_humanoid.py index a580fd57..a2e082e8 100644 --- a/gym/envs/mit_humanoid/mit_humanoid.py +++ b/gym/envs/mit_humanoid/mit_humanoid.py @@ -32,29 +32,28 @@ def _init_sampled_history_buffers(self): self.sampled_history_threshold = int( self.cfg.control.ctrl_frequency / self.cfg.env.sampled_history_frequency ) + self.J = torch.eye(self.num_dof).to(self.device) + self.J[4, 3] = 1 + self.J[9, 8] = 1 + self.J_inv_T = torch.inverse(self.J.T) def _apply_coupling(self, q, qd, q_des, qd_des, kp, kd, tau_ff): # Create a Jacobian matrix and move it to the same device as input tensors - J = torch.eye(q.shape[-1]).to(q.device) - J[4, 3] = 1 - J[9, 8] = 1 # Perform transformations using Jacobian - q = torch.matmul(q, J.T) - qd = torch.matmul(qd, J.T) - q_des = torch.matmul(q_des, J.T) - qd_des = torch.matmul(qd_des, J.T) - - # Inverse of the transpose of Jacobian - J_inv_T = torch.inverse(J.T) + q = torch.matmul(q, self.J.T) + qd = torch.matmul(qd, self.J.T) + q_des = torch.matmul(q_des, self.J.T) + qd_des = torch.matmul(qd_des, self.J.T) # Compute feed-forward torques - tau_ff = torch.matmul(J_inv_T, tau_ff.T).T + tau_ff = torch.matmul(self.J_inv_T, tau_ff.T).T # Compute kp and kd terms kp = torch.diagonal( torch.matmul( - torch.matmul(J_inv_T, torch.diag_embed(kp, dim1=-2, dim2=-1)), J_inv_T.T + torch.matmul(self.J_inv_T, torch.diag_embed(kp, dim1=-2, dim2=-1)), + self.J_inv_T.T, ), dim1=-2, dim2=-1, @@ -62,7 +61,8 @@ def _apply_coupling(self, q, qd, q_des, qd_des, kp, kd, tau_ff): kd = torch.diagonal( torch.matmul( - torch.matmul(J_inv_T, torch.diag_embed(kd, dim1=-2, dim2=-1)), J_inv_T.T + torch.matmul(self.J_inv_T, torch.diag_embed(kd, dim1=-2, dim2=-1)), + self.J_inv_T.T, ), dim1=-2, dim2=-1, @@ -70,7 +70,7 @@ def _apply_coupling(self, q, qd, q_des, qd_des, kp, kd, tau_ff): # Compute torques torques = kp * (q_des - q) + kd * (qd_des - qd) + tau_ff - torques = torch.matmul(torques, J) + torques = torch.matmul(torques, self.J) return torques diff --git a/gym/envs/mit_humanoid/mit_humanoid_config.py b/gym/envs/mit_humanoid/mit_humanoid_config.py index dc02a211..861f2826 100644 --- a/gym/envs/mit_humanoid/mit_humanoid_config.py +++ b/gym/envs/mit_humanoid/mit_humanoid_config.py @@ -112,7 +112,7 @@ class control(LeggedRobotCfg.control): } # [N*m*s/rad] ctrl_frequency = 100 - desired_sim_frequency = 1000 + desired_sim_frequency = 500 filter_gain = 0.1586 # 1: no filtering, 0: wall @@ -123,7 +123,7 @@ class commands: resampling_time = 10.0 # time before command are changed[s] class ranges: - lin_vel_x = [-0.0, 0.0] # min max [m/s] [-0.75, 0.75] + lin_vel_x = [-0.0, 4.0] # min max [m/s] [-0.75, 0.75] lin_vel_y = 0.0 # max [m/s] yaw_vel = 0.0 # max [rad/s] @@ -227,10 +227,12 @@ class MITHumanoidRunnerCfg(LeggedRobotRunnerCfg): runner_class_name = "OnPolicyRunner" class actor(LeggedRobotRunnerCfg.actor): + frequency = 100 init_noise_std = 1.0 hidden_dims = [512, 256, 128] # * can be elu, relu, selu, crelu, lrelu, tanh, sigmoid activation = "elu" + layer_norm = [True, True, False] smooth_exploration = False obs = [ @@ -238,16 +240,15 @@ class actor(LeggedRobotRunnerCfg.actor): "base_lin_vel", "base_ang_vel", "projected_gravity", - # "commands", + "commands", "dof_pos_obs", "dof_vel", - "dof_pos_history", - # "sampled_history_dof_pos", - # "sampled_history_dof_vel", - # "sampled_history_dof_pos_target", - "oscillator_obs", + # "dof_pos_history", + "sampled_history_dof_pos", + "sampled_history_dof_vel", + "sampled_history_dof_pos_target", ] - normalize_obs = True + normalize_obs = False actions = ["dof_pos_target"] disable_actions = False @@ -264,26 +265,28 @@ class critic(LeggedRobotRunnerCfg.critic): hidden_dims = [512, 256, 128] # * can be elu, relu, selu, crelu, lrelu, tanh, sigmoid activation = "elu" + layer_norm = [True, True, False] obs = [ "base_height", "base_lin_vel", "base_ang_vel", "projected_gravity", - "commands", "dof_pos_obs", "dof_vel", - "dof_pos_history", + "sampled_history_dof_pos", + "sampled_history_dof_vel", + "sampled_history_dof_pos_target", ] - normalize_obs = True + normalize_obs = False class reward: class weights: - # tracking_lin_vel = 0.0 - # tracking_ang_vel = 1.5 + tracking_lin_vel = 4.0 + tracking_ang_vel = 0.5 # orientation = 1.0 torques = 5.0e-4 - # min_base_height = 1.5 + min_base_height = 1.5 action_rate = 1e-3 action_rate2 = 1e-3 lin_vel_z = 0.0 @@ -292,8 +295,6 @@ class weights: # stand_still = 0.25 dof_pos_limits = 0.25 dof_near_home = 0.25 - stance = 1.0 - swing = 1.0 hips_forward = 0.0 walk_freq = 0.0 # 2.5 @@ -305,12 +306,8 @@ class algorithm(LeggedRobotRunnerCfg.algorithm): gamma = 0.99 lam = 0.95 # shared - batch_size = 2**15 - max_gradient_steps = 24 - # new - storage_size = 2**17 # new - batch_size = 2**15 # new - + batch_size = 4096 + max_gradient_steps = 48 clip_param = 0.2 learning_rate = 1.0e-3 max_grad_norm = 1.0 diff --git a/learning/modules/QRCritics.py b/learning/modules/QRCritics.py index d2ed9378..9dd51489 100644 --- a/learning/modules/QRCritics.py +++ b/learning/modules/QRCritics.py @@ -19,7 +19,7 @@ def init_weights(m): m.bias.data.fill_(0.01) -class Critic(nn.Module): +class Critic2(nn.Module): def __init__( self, num_obs, From 08bb974613f7302253bfa26ca17a3edf4bf15236 Mon Sep 17 00:00:00 2001 From: sheim Date: Wed, 22 Oct 2025 10:44:58 -0400 Subject: [PATCH 36/77] fixed config policy of osc --- .../mini_cheetah/mini_cheetah_osc_config.py | 67 ++++++++++++++++++- scripts/play.py | 3 +- 2 files changed, 67 insertions(+), 3 deletions(-) diff --git a/gym/envs/mini_cheetah/mini_cheetah_osc_config.py b/gym/envs/mini_cheetah/mini_cheetah_osc_config.py index dcc33da7..fdf03c8a 100644 --- a/gym/envs/mini_cheetah/mini_cheetah_osc_config.py +++ b/gym/envs/mini_cheetah/mini_cheetah_osc_config.py @@ -15,7 +15,7 @@ class viewer: class env(MiniCheetahCfg.env): num_envs = 4096 num_actuators = 12 - episode_length_s = 4.0 + episode_length_s = 5.0 env_spacing = 3.0 class terrain(MiniCheetahCfg.terrain): @@ -174,7 +174,6 @@ class actor(MiniCheetahRunnerCfg.actor): "commands", "dof_pos_obs", "dof_vel", - "oscillator_obs", "dof_pos_target", ] normalize_obs = False @@ -239,6 +238,70 @@ class weights: class termination_weight: termination = 0.15 + # class policy: + # hidden_dims = [256, 256, 128] + # critic_hidden_dims = [256, 256, 128] + # # * can be elu, relu, selu, crelu, lrelu, tanh, sigmoid + # activation = "elu" + # smooth_exploration = False + + # obs = [ + # "base_ang_vel", + # "projected_gravity", + # "commands", + # "dof_pos_obs", + # "dof_vel", + # "oscillator_obs", + # "dof_pos_target", + # ] + + # critic_obs = [ + # "base_height", + # "base_lin_vel", + # "base_ang_vel", + # "projected_gravity", + # "commands", + # "dof_pos_obs", + # "dof_vel", + # "oscillator_obs", + # "oscillators_vel", + # "dof_pos_target", + # ] + + # actions = ["dof_pos_target"] + + # class noise: + # pass + + # class reward: + # class weights: + # tracking_lin_vel = 4.0 + # tracking_ang_vel = 2.0 + # lin_vel_z = 0.0 + # ang_vel_xy = 0.01 + # orientation = 1.0 + # torques = 5.0e-7 + # dof_vel = 0.0 + # min_base_height = 1.5 + # collision = 0 + # action_rate = 0.01 # -0.01 + # action_rate2 = 0.001 # -0.001 + # stand_still = 0.0 + # dof_pos_limits = 0.0 + # feet_contact_forces = 0.0 + # dof_near_home = 0.0 + # swing_grf = 5.0 + # stance_grf = 5.0 + # swing_velocity = 0.0 + # stance_velocity = 0.0 + # coupled_grf = 0.0 # 8. + # enc_pace = 0.0 + # cursorial = 0.25 + # standing_torques = 0.0 # 1.e-5 + + # class termination_weight: + # termination = 15.0 / 100.0 + class algorithm(MiniCheetahRunnerCfg.algorithm): # both gamma = 0.99 diff --git a/scripts/play.py b/scripts/play.py index 37882f7e..833e6641 100644 --- a/scripts/play.py +++ b/scripts/play.py @@ -11,7 +11,7 @@ def setup(args): env_cfg, train_cfg = task_registry.create_cfgs(args) env_cfg.env.num_envs = 32 if hasattr(env_cfg, "push_robots"): - env_cfg.push_robots.toggle = False + env_cfg.push_robots.toggle = True if hasattr(env_cfg, "commands"): env_cfg.commands.resampling_time = 9999 env_cfg.env.episode_length_s = 50 @@ -22,6 +22,7 @@ def setup(args): train_cfg.runner.resume = True train_cfg.logging.enable_local_saving = False runner = task_registry.make_alg_runner(env, train_cfg) + # runner.actor_cfg["disable_actions"] = True # * switch to evaluation mode (dropout for example) runner.switch_to_eval() From de3212e5d2a094cbaa057f45a07520a8dbff36d0 Mon Sep 17 00:00:00 2001 From: sheim Date: Thu, 23 Oct 2025 13:50:14 -0400 Subject: [PATCH 37/77] added option back in --- learning/algorithms/ppo2.py | 16 +++++++++++++++- 1 file changed, 15 insertions(+), 1 deletion(-) diff --git a/learning/algorithms/ppo2.py b/learning/algorithms/ppo2.py index 6c64e334..84f06954 100644 --- a/learning/algorithms/ppo2.py +++ b/learning/algorithms/ppo2.py @@ -81,7 +81,21 @@ def update_critic(self, data): max_gradient_steps=self.max_gradient_steps, ) for batch in generator: - value_loss = self.critic.loss_fn(batch["critic_obs"], batch["returns"]) + # todo validate and check this + if self.use_clipped_value_loss: + value_clipped = batch["values"] + ( + self.critic.evaluate(batch["critic_obs"]) - batch["values"] + ).clamp(-self.clip_param, self.clip_param) + value_losses_clipped = (value_clipped - batch["returns"]).pow(2) + value_losses = ( + (self.critic.evaluate(batch["critic_obs"]) - batch["returns"]) + .pow(2) + .mean() + ) + value_loss = torch.max(value_losses, value_losses_clipped).mean() + else: + value_loss = self.critic.loss_fn(batch["critic_obs"], batch["returns"]) + self.critic_optimizer.zero_grad() value_loss.backward() nn.utils.clip_grad_norm_(self.critic.parameters(), self.max_grad_norm) From c0bda9085e2841b9af8483c81322a781a299eece Mon Sep 17 00:00:00 2001 From: sheim Date: Fri, 24 Oct 2025 10:35:24 -0400 Subject: [PATCH 38/77] large refactor to get osc working again --- gym/envs/mini_cheetah/mini_cheetah.py | 7 + gym/envs/mini_cheetah/mini_cheetah_config.py | 8 +- gym/envs/mini_cheetah/mini_cheetah_osc.py | 51 ++---- .../mini_cheetah/mini_cheetah_osc_config.py | 166 +++++++----------- 4 files changed, 90 insertions(+), 142 deletions(-) diff --git a/gym/envs/mini_cheetah/mini_cheetah.py b/gym/envs/mini_cheetah/mini_cheetah.py index bea90140..645e6093 100644 --- a/gym/envs/mini_cheetah/mini_cheetah.py +++ b/gym/envs/mini_cheetah/mini_cheetah.py @@ -40,6 +40,13 @@ def _reward_tracking_lin_vel(self): error = torch.sum(torch.square(error), dim=1) return torch.exp(-error / self.cfg.reward_settings.tracking_sigma) + def _reward_tracking_ang_vel(self): + """Tracking of angular velocity commands (yaw)""" + ang_vel_error = torch.square( + (self.commands[:, 2] - self.base_ang_vel[:, 2]) / 5.0 + ) + return self._sqrdexp(ang_vel_error) + def _reward_dof_vel(self): """Penalize dof velocities""" return torch.sum(self._sqrdexp(self.dof_vel / self.scales["dof_vel"]), dim=1) diff --git a/gym/envs/mini_cheetah/mini_cheetah_config.py b/gym/envs/mini_cheetah/mini_cheetah_config.py index ba2655f7..5ec7ff18 100644 --- a/gym/envs/mini_cheetah/mini_cheetah_config.py +++ b/gym/envs/mini_cheetah/mini_cheetah_config.py @@ -10,7 +10,7 @@ class MiniCheetahCfg(LeggedRobotCfg): class env(LeggedRobotCfg.env): num_envs = 2**12 num_actuators = 12 - episode_length_s = 4 + episode_length_s = 10 class terrain(LeggedRobotCfg.terrain): mesh_type = "plane" @@ -74,9 +74,9 @@ class ranges: yaw_vel = 3 # max [rad/s] class push_robots: - toggle = True - interval_s = 15 - max_push_vel_xy = 0.05 + toggle = False + interval_s = 1 + max_push_vel_xy = 0.5 push_box_dims = [0.3, 0.1, 0.1] # x,y,z [m] class domain_rand: diff --git a/gym/envs/mini_cheetah/mini_cheetah_osc.py b/gym/envs/mini_cheetah/mini_cheetah_osc.py index acec0b8a..df71e728 100644 --- a/gym/envs/mini_cheetah/mini_cheetah_osc.py +++ b/gym/envs/mini_cheetah/mini_cheetah_osc.py @@ -6,7 +6,7 @@ from isaacgym import gymtorch -MINI_CHEETAH_MASS = 8.292 * 9.81 # Weight of mini cheetah in Newtons +MINI_CHEETAH_WEIGHT = 8.292 * 9.81 # Weight of mini cheetah in Newtons class MiniCheetahOsc(MiniCheetah): @@ -209,6 +209,21 @@ def _resample_commands(self, env_ids): 0, len(possible_commands), (len(env_ids), 1), device=self.device ) ] + # add some gaussian noise to the commands + self.commands[env_ids, 0:1] += ( + torch.randn((len(env_ids), 1), device=self.device) * self.cfg.commands.var + ) + + # possible_commands = torch.tensor(self.command_ranges["lin_vel_y"], + # device=self.device) + # self.commands[env_ids, 1:2] = possible_commands[torch.randint( + # 0, len(possible_commands), (len(env_ids), 1), + # device=self.device)] + # possible_commands = torch.tensor(self.command_ranges["yaw_vel"], + # device=self.device) + # self.commands[env_ids, 0:1] = possible_commands[torch.randint( + # 0, len(possible_commands), (len(env_ids), 1), + # device=self.device)] if 0 in self.cfg.commands.ranges.lin_vel_x: # * with 20% chance, reset to 0 commands except for forward @@ -262,7 +277,7 @@ def perturb_base_velocity(self, velocity_delta, env_ids=None): def _compute_grf(self, grf_norm=True): grf = torch.norm(self.contact_forces[:, self.feet_indices, :], dim=-1) if grf_norm: - return torch.clamp_max(grf / MINI_CHEETAH_MASS, 1.0) + return torch.clamp_max(grf / MINI_CHEETAH_WEIGHT, 1.0) else: return grf @@ -324,38 +339,6 @@ def _reward_coupled_grf(self): prod = torch.prod(torch.clip(combined_rew, 0, 1), dim=1) return prod - torch.ones_like(prod) - def _reward_swing_velocity(self): - """Reward non-zero end effector velocity during swing (0 to pi)""" - # velocity = torch.tanh(torch.norm(self.end_effector_lin_vel, dim=-1)) - velocity = torch.zeros_like( - self.oscillators - ) # TODO: Grab velocity from AJ V2 code - phase_bool = torch.lt(self.oscillators, torch.pi).int() - phase_sin = torch.maximum( - torch.zeros_like(self.oscillators), torch.sin(self.oscillators) - ) - if self.cfg.osc.osc_bool: - rew = phase_bool * velocity - else: - rew = phase_sin * velocity - return torch.mean(rew, dim=1) - - def _reward_stance_velocity(self): - """Reward zero end effector velocity during swing (pi to 2pi)""" - # velocity = torch.tanh(torch.norm(self.end_effector_lin_vel, dim=-1)) - velocity = torch.zeros_like( - self.oscillators - ) # TODO: Grab velocity from AJ V2 code - ph_bool = torch.gt(self.oscillators, torch.pi).int() - ph_sin = torch.maximum( - torch.zeros_like(self.oscillators), -torch.sin(self.oscillators) - ) - if self.cfg.osc.osc_bool: - rew = ph_bool * velocity - else: - rew = ph_sin * velocity - return -torch.mean(rew, dim=1) - def _reward_dof_vel(self): return super()._reward_dof_vel() * self._switch() diff --git a/gym/envs/mini_cheetah/mini_cheetah_osc_config.py b/gym/envs/mini_cheetah/mini_cheetah_osc_config.py index fdf03c8a..7bca16ac 100644 --- a/gym/envs/mini_cheetah/mini_cheetah_osc_config.py +++ b/gym/envs/mini_cheetah/mini_cheetah_osc_config.py @@ -15,7 +15,7 @@ class viewer: class env(MiniCheetahCfg.env): num_envs = 4096 num_actuators = 12 - episode_length_s = 5.0 + episode_length_s = 10.0 env_spacing = 3.0 class terrain(MiniCheetahCfg.terrain): @@ -23,6 +23,7 @@ class terrain(MiniCheetahCfg.terrain): # mesh_type = 'trimesh' # none, plane, heightfield or trimesh class init_state(MiniCheetahCfg.init_state): + timeout_reset_ratio = 0.75 reset_mode = "reset_to_range" timeout_reset_ratio = 0.75 # * default COM for basic initialization @@ -38,7 +39,7 @@ class init_state(MiniCheetahCfg.init_state): # * initialization for random range setup dof_pos_range = { - "haa": [-0.01, 0.01], + "haa": [0.0, 0.01], "hfe": [-0.785398, -0.785398], "kfe": [1.596976, 1.596976], } @@ -46,7 +47,7 @@ class init_state(MiniCheetahCfg.init_state): root_pos_range = [ [0.0, 0.0], # x [0.0, 0.0], # y - [0.35, 0.35], # z + [0.375, 0.375], # z [0.0, 0.0], # roll [0.0, 0.0], # pitch [0.0, 0.0], # yaw @@ -54,7 +55,7 @@ class init_state(MiniCheetahCfg.init_state): root_vel_range = [ [-0.5, 2.0], # x [0.0, 0.0], # y - [-0.05, 0.05], # z + [0.0, 0.0], # z [0.0, 0.0], # roll [0.0, 0.0], # pitch [0.0, 0.0], # yaw @@ -69,18 +70,15 @@ class control(MiniCheetahCfg.control): class osc: process_noise_std = 0.25 + grf_threshold = 0.1 # 20. # Normalized to body weight # oscillator parameters - omega = 3 # 0.5 #3.5 # in Hz - coupling = 1 # 0.02 - osc_bool = False - grf_bool = False + omega = 3 # gets overwritten + coupling = 1 # gets overwritten + osc_bool = False # not used in paper + grf_bool = False # not used in paper randomize_osc_params = False - grf_threshold = 0.1 # 20. # Normalized to body weight omega_range = [1.0, 4.0] # [0.0, 10.] - coupling_range = [ - 0.0, - 1.0, - ] # with normalized grf, can have omega/coupling on same scale + coupling_range = [0.0, 1.0] offset_range = [0.0, 0.0] stop_threshold = 0.5 omega_stop = 1.0 @@ -100,12 +98,12 @@ class osc: init_to = "random" init_w_offset = True - class commands(MiniCheetahCfg.commands): + class commands: resampling_time = 3.0 # time before command are changed[s] var = 1.0 - class ranges(MiniCheetahCfg.commands.ranges): - lin_vel_x = [-3.0, -1.0, 0.0, 1.0, 3.0] # min max [m/s] + class ranges: + lin_vel_x = [-1.0, 0.0, 1.0, 3.0] # min max [m/s] lin_vel_y = 1.0 # [-1., 0, 1.] # max [m/s] yaw_vel = 3.0 # [-6., -3., 0., 3., 6.] # max [rad/s] @@ -155,7 +153,18 @@ class reward_settings(MiniCheetahCfg.reward_settings): switch_scale = 0.5 class scaling(MiniCheetahCfg.scaling): - pass + base_ang_vel = [0.3, 0.3, 0.1] + base_lin_vel = BASE_HEIGHT_REF + # dof_vel = 100. + # dof_vel = 4*[41, 41, 27] # ought to be roughly max expected speed. + dof_vel = 4 * [2.0, 2.0, 4.0] + base_height = 0.3 + dof_pos = 4 * [0.2, 0.3, 0.3] # hip-abad, hip-pitch, knee + dof_pos_obs = dof_pos + dof_pos_target = 4 * [0.2, 0.3, 0.3] + tau_ff = 4 * [18, 18, 28] # hip-abad, hip-pitch, knee + # commands = [base_lin_vel, base_lin_vel, base_ang_vel] + commands = [3, 1, 3] # [base_lin_vel, base_lin_vel, base_ang_vel] class MiniCheetahOscRunnerCfg(MiniCheetahRunnerCfg): @@ -166,15 +175,31 @@ class actor(MiniCheetahRunnerCfg.actor): hidden_dims = [256, 256, 128] # * can be elu, relu, selu, crelu, lrelu, tanh, sigmoid activation = "elu" - smooth_exploration = False - exploration_sample_freq = 16 + # obs = [ + # "base_height", + # "base_lin_vel", + # "base_ang_vel", + # "projected_gravity", + # "commands", + # "dof_pos_obs", + # "dof_vel", + # "oscillator_obs", + # "dof_pos_target", + # ] obs = [ + "base_height", + "base_lin_vel", "base_ang_vel", "projected_gravity", "commands", "dof_pos_obs", "dof_vel", + "oscillator_obs", "dof_pos_target", + # "osc_omega", + # "osc_coupling" + # "oscillators_vel", + # "grf", ] normalize_obs = False @@ -214,11 +239,11 @@ class weights: tracking_lin_vel = 4.0 tracking_ang_vel = 2.0 lin_vel_z = 0.0 - ang_vel_xy = 0.01 + ang_vel_xy = 0.0 orientation = 1.0 torques = 5.0e-7 dof_vel = 0.0 - min_base_height = 1.5 + min_base_height = 1.0 collision = 0 action_rate = 0.01 # -0.01 action_rate2 = 0.001 # -0.001 @@ -226,8 +251,8 @@ class weights: dof_pos_limits = 0.0 feet_contact_forces = 0.0 dof_near_home = 0.0 - swing_grf = 5.0 - stance_grf = 5.0 + swing_grf = 1.0 + stance_grf = 1.0 swing_velocity = 0.0 stance_velocity = 0.0 coupled_grf = 0.0 # 8. @@ -238,90 +263,23 @@ class weights: class termination_weight: termination = 0.15 - # class policy: - # hidden_dims = [256, 256, 128] - # critic_hidden_dims = [256, 256, 128] - # # * can be elu, relu, selu, crelu, lrelu, tanh, sigmoid - # activation = "elu" - # smooth_exploration = False - - # obs = [ - # "base_ang_vel", - # "projected_gravity", - # "commands", - # "dof_pos_obs", - # "dof_vel", - # "oscillator_obs", - # "dof_pos_target", - # ] - - # critic_obs = [ - # "base_height", - # "base_lin_vel", - # "base_ang_vel", - # "projected_gravity", - # "commands", - # "dof_pos_obs", - # "dof_vel", - # "oscillator_obs", - # "oscillators_vel", - # "dof_pos_target", - # ] - - # actions = ["dof_pos_target"] - - # class noise: - # pass - - # class reward: - # class weights: - # tracking_lin_vel = 4.0 - # tracking_ang_vel = 2.0 - # lin_vel_z = 0.0 - # ang_vel_xy = 0.01 - # orientation = 1.0 - # torques = 5.0e-7 - # dof_vel = 0.0 - # min_base_height = 1.5 - # collision = 0 - # action_rate = 0.01 # -0.01 - # action_rate2 = 0.001 # -0.001 - # stand_still = 0.0 - # dof_pos_limits = 0.0 - # feet_contact_forces = 0.0 - # dof_near_home = 0.0 - # swing_grf = 5.0 - # stance_grf = 5.0 - # swing_velocity = 0.0 - # stance_velocity = 0.0 - # coupled_grf = 0.0 # 8. - # enc_pace = 0.0 - # cursorial = 0.25 - # standing_torques = 0.0 # 1.e-5 - - # class termination_weight: - # termination = 15.0 / 100.0 - - class algorithm(MiniCheetahRunnerCfg.algorithm): - # both - gamma = 0.99 - lam = 0.98 - batch_size = 2**15 - max_gradient_steps = 24 - # new - storage_size = 2**17 # new - batch_size = 2**15 # new - + class algorithm: + # training params + value_loss_coef = 1.0 + use_clipped_value_loss = False clip_param = 0.2 - learning_rate = 1.0e-3 - max_grad_norm = 1.0 - # Critic - use_clipped_value_loss = True - # Actor entropy_coef = 0.01 - schedule = "adaptive" # could be adaptive, fixed + num_learning_epochs = 4 + # mini batch size = num_envs*nsteps/nminibatches + num_mini_batches = 8 + max_gradient_steps = 32 + learning_rate = 1.0e-4 + schedule = "adaptive" # can be adaptive, fixed + discount_horizon = 1.0 + GAE_bootstrap_horizon = 2.0 desired_kl = 0.01 - lr_range = [2e-5, 1e-2] + max_grad_norm = 1.0 + lr_range = [1e-5, 5e-3] lr_ratio = 1.5 class runner(MiniCheetahRunnerCfg.runner): From ec524f60302cd8eaa33382094ff0564aff6402e0 Mon Sep 17 00:00:00 2001 From: sheim Date: Fri, 24 Oct 2025 12:34:47 -0400 Subject: [PATCH 39/77] copilot rev fixes --- gym/envs/mini_cheetah/mini_cheetah_osc_config.py | 3 +-- learning/algorithms/ppo2.py | 6 ++---- 2 files changed, 3 insertions(+), 6 deletions(-) diff --git a/gym/envs/mini_cheetah/mini_cheetah_osc_config.py b/gym/envs/mini_cheetah/mini_cheetah_osc_config.py index 7bca16ac..50debce7 100644 --- a/gym/envs/mini_cheetah/mini_cheetah_osc_config.py +++ b/gym/envs/mini_cheetah/mini_cheetah_osc_config.py @@ -25,7 +25,6 @@ class terrain(MiniCheetahCfg.terrain): class init_state(MiniCheetahCfg.init_state): timeout_reset_ratio = 0.75 reset_mode = "reset_to_range" - timeout_reset_ratio = 0.75 # * default COM for basic initialization pos = [0.0, 0.0, 0.35] # x,y,z [m] rot = [0.0, 0.0, 0.0, 1.0] # x,y,z,w [quat] @@ -266,7 +265,7 @@ class termination_weight: class algorithm: # training params value_loss_coef = 1.0 - use_clipped_value_loss = False + use_clipped_value_loss = True clip_param = 0.2 entropy_coef = 0.01 num_learning_epochs = 4 diff --git a/learning/algorithms/ppo2.py b/learning/algorithms/ppo2.py index 84f06954..bcaaa0e7 100644 --- a/learning/algorithms/ppo2.py +++ b/learning/algorithms/ppo2.py @@ -88,10 +88,8 @@ def update_critic(self, data): ).clamp(-self.clip_param, self.clip_param) value_losses_clipped = (value_clipped - batch["returns"]).pow(2) value_losses = ( - (self.critic.evaluate(batch["critic_obs"]) - batch["returns"]) - .pow(2) - .mean() - ) + self.critic.evaluate(batch["critic_obs"]) - batch["returns"] + ).pow(2) value_loss = torch.max(value_losses, value_losses_clipped).mean() else: value_loss = self.critic.loss_fn(batch["critic_obs"], batch["returns"]) From d9e26628279548780d6bb9c8199e85c2dc2cd321 Mon Sep 17 00:00:00 2001 From: sheim Date: Fri, 31 Oct 2025 15:13:47 -0400 Subject: [PATCH 40/77] two test fixes: 1) reward shape in osc, 2) underactuation in fixed_robot --- gym/envs/mini_cheetah/mini_cheetah_osc.py | 5 +++++ 1 file changed, 5 insertions(+) diff --git a/gym/envs/mini_cheetah/mini_cheetah_osc.py b/gym/envs/mini_cheetah/mini_cheetah_osc.py index df71e728..77866ca3 100644 --- a/gym/envs/mini_cheetah/mini_cheetah_osc.py +++ b/gym/envs/mini_cheetah/mini_cheetah_osc.py @@ -288,6 +288,11 @@ def _switch(self): / self.cfg.reward_settings.switch_scale ) + def _reward_ang_vel_xy(self): + """Penalize xy axes base angular velocity""" + error = self._sqrdexp(self.base_ang_vel[:, :2] / self.scales["base_ang_vel"][0]) + return torch.sum(error, dim=1) + def _reward_cursorial(self): # penalize the abad joints being away from 0 return -torch.mean( From 3c4e6e57b924c8da92dd8fc137904127ccd55c50 Mon Sep 17 00:00:00 2001 From: sheim Date: Fri, 31 Oct 2025 15:34:47 -0400 Subject: [PATCH 41/77] modernized ruff formatting (apparently...) --- gym/utils/helpers.py | 3 +-- gym/utils/interfaces/GamepadInterface.py | 5 ++--- gym/utils/interfaces/KeyboardInterface.py | 2 +- gym/utils/task_registry.py | 2 +- learning/utils/logger/Logger.py | 5 ++--- tests/integration_tests/test_runner_integration.py | 12 ++++++------ 6 files changed, 13 insertions(+), 16 deletions(-) diff --git a/gym/utils/helpers.py b/gym/utils/helpers.py index 1e421dac..25391ffb 100644 --- a/gym/utils/helpers.py +++ b/gym/utils/helpers.py @@ -242,8 +242,7 @@ def get_args(custom_parameters=None): "name": "--rl_device", "type": str, "default": "cuda:0", - "help": "Device used by the RL algorithm, " - "(cpu, gpu, cuda:0, cuda:1 etc..)", + "help": "Device used by the RL algorithm, (cpu, gpu, cuda:0, cuda:1 etc..)", }, { "name": "--num_envs", diff --git a/gym/utils/interfaces/GamepadInterface.py b/gym/utils/interfaces/GamepadInterface.py index 4790b6e1..9b990b1c 100644 --- a/gym/utils/interfaces/GamepadInterface.py +++ b/gym/utils/interfaces/GamepadInterface.py @@ -11,7 +11,7 @@ def __init__(self, env): print("__________________________________________________________") if pygame.joystick.get_count() == 1: print("Using gamepad interface, " + "overriding default comand settings") - print("left joystick: forward, strafe left, " "backward, strafe right") + print("left joystick: forward, strafe left, backward, strafe right") print("right joystick (left/right): yaw left/right") print("back button: reset environments") print("start button: quit") @@ -26,8 +26,7 @@ def __init__(self, env): elif pygame.joystick.get_count() > 1: print( - "WARNING: you have more than one gamepad plugged in." - "Please unplug one." + "WARNING: you have more than one gamepad plugged in.Please unplug one." ) else: print("WARNING: failed to initialize gamepad.") diff --git a/gym/utils/interfaces/KeyboardInterface.py b/gym/utils/interfaces/KeyboardInterface.py index b1d506b7..d2e3ce99 100644 --- a/gym/utils/interfaces/KeyboardInterface.py +++ b/gym/utils/interfaces/KeyboardInterface.py @@ -21,7 +21,7 @@ def __init__(self, env): print("______________________________________________________________") print("Using keyboard interface, overriding default comand settings") print("commands are in 1/5 increments of max.") - print("WASD: forward, strafe left, " "backward, strafe right") + print("WASD: forward, strafe left, backward, strafe right") print("QE: yaw left/right") print("R: reset environments") print("ESC: quit") diff --git a/gym/utils/task_registry.py b/gym/utils/task_registry.py index 6c2025ba..477049f3 100644 --- a/gym/utils/task_registry.py +++ b/gym/utils/task_registry.py @@ -174,7 +174,7 @@ def set_control_and_sim_dt(self, env_cfg, train_cfg): if env_cfg.sim_dt != 1.0 / env_cfg.control.desired_sim_frequency: print( f"****** Simulation dt adjusted from " - f"{1.0/env_cfg.control.desired_sim_frequency}" + f"{1.0 / env_cfg.control.desired_sim_frequency}" f" to {env_cfg.sim_dt}." ) diff --git a/learning/utils/logger/Logger.py b/learning/utils/logger/Logger.py index bcaa56a2..f59ab975 100644 --- a/learning/utils/logger/Logger.py +++ b/learning/utils/logger/Logger.py @@ -90,8 +90,7 @@ def separator(subtitle="", marker="-"): dashes_each_side = (width - subtitle_length) // 2 log_string += "\n" log_string += ( - f"{marker * dashes_each_side} {subtitle} " - f"{marker * dashes_each_side}\n" + f"{marker * dashes_each_side} {subtitle} {marker * dashes_each_side}\n" ) separator(f"Iteration {self.iteration_counter}/{self.tot_iter}", "#") @@ -114,7 +113,7 @@ def separator(subtitle="", marker="-"): tot_t = self.timer.get_time("iteration") col_time = self.timer.get_time("collection") learn_time = self.timer.get_time("learning") - time_string = f"(sim: {col_time:.2f}" f", learn:{learn_time:.2f})" + time_string = f"(sim: {col_time:.2f}, learn:{learn_time:.2f})" format_log_entry("total time", tot_t, time_string) format_log_entry( diff --git a/tests/integration_tests/test_runner_integration.py b/tests/integration_tests/test_runner_integration.py index ce645b5c..611108e5 100644 --- a/tests/integration_tests/test_runner_integration.py +++ b/tests/integration_tests/test_runner_integration.py @@ -78,9 +78,9 @@ def test_default_integration_settings(self, args): model_7_path = os.path.join(runner.log_dir, "model_7.pt") model_8_path = os.path.join(runner.log_dir, "model_8.pt") - assert os.path.exists(model_0_path), ( - f"{model_0_path} " "(pre-iteration) was not saved" - ) + assert os.path.exists( + model_0_path + ), f"{model_0_path} (pre-iteration) was not saved" assert not os.path.exists(model_1_path), f"{model_1_path} was saved" assert not os.path.exists(model_2_path), f"{model_2_path} was saved" assert os.path.exists(model_3_path), f"{model_3_path} was not saved" @@ -88,9 +88,9 @@ def test_default_integration_settings(self, args): assert not os.path.exists(model_5_path), f"{model_5_path} was saved" assert os.path.exists(model_6_path), f"{model_6_path} was not saved" assert not os.path.exists(model_7_path), f"{model_7_path} was saved" - assert os.path.exists(model_8_path), ( - f"{model_5_path}" "(last iteration) was not saved" - ) + assert os.path.exists( + model_8_path + ), f"{model_5_path}(last iteration) was not saved" obs = torch.randn_like(runner.get_obs(runner.actor_cfg["obs"])) actions_first = runner.alg.actor.act_inference(obs).cpu().clone() From 0606552fb91eff852a287626f4233b08241a3af8 Mon Sep 17 00:00:00 2001 From: sheim Date: Fri, 31 Oct 2025 15:45:51 -0400 Subject: [PATCH 42/77] redo regression test to be standalone instead of running via CI --- tests/regression_tests/run_n_iterations.py | 8 ++++ .../test_generated_torch_files.py | 37 ++++++++++++++----- 2 files changed, 35 insertions(+), 10 deletions(-) diff --git a/tests/regression_tests/run_n_iterations.py b/tests/regression_tests/run_n_iterations.py index 29e2f424..bf4511e9 100644 --- a/tests/regression_tests/run_n_iterations.py +++ b/tests/regression_tests/run_n_iterations.py @@ -1,3 +1,5 @@ +from pathlib import Path + from gym.envs import __init__ # noqa: F401 from gym.utils import get_args, task_registry import torch @@ -12,6 +14,10 @@ def setup(): } ] args = get_args(custom_parameter) + if not args.output_tensor_file: + args.output_tensor_file = Path(__file__).with_name("main_output.pt") + else: + args.output_tensor_file = Path(args.output_tensor_file) args.task = "mini_cheetah" args.seed = 0 args.headless = True @@ -29,6 +35,8 @@ def setup(): def _run_training_test(output_tensor_file, env, runner): + output_tensor_file = Path(output_tensor_file) + output_tensor_file.parent.mkdir(parents=True, exist_ok=True) # * take a full iteration step runner.learn() diff --git a/tests/regression_tests/test_generated_torch_files.py b/tests/regression_tests/test_generated_torch_files.py index 11b9ff5a..cf640ed3 100644 --- a/tests/regression_tests/test_generated_torch_files.py +++ b/tests/regression_tests/test_generated_torch_files.py @@ -1,15 +1,32 @@ +import subprocess +import sys +from pathlib import Path + import torch +REFERENCE_FILE = Path(__file__).with_name("main_output.pt") +RUN_SCRIPT = Path(__file__).with_name("run_n_iterations.py") + + +def _generate_candidate_tensor(output_path: Path) -> None: + cmd = [ + sys.executable, + str(RUN_SCRIPT), + "--output_tensor_file", + str(output_path), + ] + subprocess.run(cmd, check=True) + -def compare_tensors(file1, file2): - tensor1 = torch.load(file1, weights_only=True) - tensor2 = torch.load(file2, weights_only=True) - assert torch.all( - torch.eq(tensor1, tensor2) - ), f"Tensors in {file1} and {file2} are not equal." +def _load_tensor(path: Path): + return torch.load(path, weights_only=True) -def test_tensor_comparison(): - file1 = "main_output.pt" - file2 = "pr_output.pt" - compare_tensors(file1, file2) +def test_generated_tensor_matches_reference(tmp_path): + candidate_path = tmp_path / "generated_output.pt" + _generate_candidate_tensor(candidate_path) + reference = _load_tensor(REFERENCE_FILE) + candidate = _load_tensor(candidate_path) + assert torch.equal( + reference, candidate + ), f"Tensors in {REFERENCE_FILE} and {candidate_path} differ." From 7cf29c7add38cdd6c7fefb817075ea0022f282ea Mon Sep 17 00:00:00 2001 From: sheim Date: Fri, 31 Oct 2025 15:51:09 -0400 Subject: [PATCH 43/77] remove self-hosted unit tests, add cheap tests (gym, learning) --- .github/workflows/PR_tests.yml | 23 --------------- .github/workflows/push_tests.yml | 37 ------------------------ .github/workflows/regression_tests.yml | 40 -------------------------- .github/workflows/unit_tests.yml | 33 +++++++++++++++++++++ 4 files changed, 33 insertions(+), 100 deletions(-) delete mode 100644 .github/workflows/PR_tests.yml delete mode 100644 .github/workflows/push_tests.yml delete mode 100644 .github/workflows/regression_tests.yml create mode 100644 .github/workflows/unit_tests.yml diff --git a/.github/workflows/PR_tests.yml b/.github/workflows/PR_tests.yml deleted file mode 100644 index 5730c8b3..00000000 --- a/.github/workflows/PR_tests.yml +++ /dev/null @@ -1,23 +0,0 @@ -name: GPUgym Unit Testing -run-name: PR Unit Testing -on: - pull_request: - branches: [ "main", "dev" ] - push: - branches: [ "main", "dev" ] - -jobs: - Self-hosted-unit-testing: - runs-on: [self-hosted, Linux, X64] - - steps: - - name: Check out repo - uses: actions/checkout@v3 - - - name: Integration Testing gpuGym - run: | - source ~/anaconda3/etc/profile.d/conda.sh - conda activate CI - pip install -r requirements.txt - pip install -e . - pytest tests/integration_tests \ No newline at end of file diff --git a/.github/workflows/push_tests.yml b/.github/workflows/push_tests.yml deleted file mode 100644 index 0e60fb0a..00000000 --- a/.github/workflows/push_tests.yml +++ /dev/null @@ -1,37 +0,0 @@ -name: Basic Unit Testing -run-name: Basic Unit Testing -on: - push: - branches: - - '*' - -jobs: - - Self-hosted-unit-testing: - runs-on: [self-hosted, Linux, X64] - - steps: - - name: Check out repo - uses: actions/checkout@v3 - - - name: Unit Testing Gym - run: | - source ~/anaconda3/etc/profile.d/conda.sh - conda activate CI - pip install -r requirements.txt - pip install -e . - pytest gym - pytest learning - - ruff-linting: - runs-on: [self-hosted, Linux, X64] - - steps: - - name: Check out repo - uses: actions/checkout@v3 - - - name: Lint with ruff - run: | - source ~/anaconda3/etc/profile.d/conda.sh - conda activate CI - ruff check . \ No newline at end of file diff --git a/.github/workflows/regression_tests.yml b/.github/workflows/regression_tests.yml deleted file mode 100644 index 778a88be..00000000 --- a/.github/workflows/regression_tests.yml +++ /dev/null @@ -1,40 +0,0 @@ -name: gpuGym Regression Testing -run-name: PR Regression Testing -on: - pull_request: - branches: [ "main", "dev" ] - -jobs: - Self-hosted-unit-testing: - runs-on: [self-hosted, Linux, X64] - - steps: - - name: Check out repo - uses: actions/checkout@v3 - - - name: Checkout main and run test - run: | - git checkout main - source ~/anaconda3/etc/profile.d/conda.sh - conda activate CI - pip install -r requirements.txt - pip install -e . - python tests/regression_tests/run_n_iterations.py --output_tensor_file="main_output.pt" - - - name: Checkout PR and run test - run: | - git fetch --unshallow - git checkout ${{ github.event.pull_request.head.sha }} - source ~/anaconda3/etc/profile.d/conda.sh - conda activate CI - pip install -r requirements.txt - pip install -e . - python tests/regression_tests/run_n_iterations.py --output_tensor_file="pr_output.pt" - - - name: Compare results - run: | - source ~/anaconda3/etc/profile.d/conda.sh - conda activate CI - pip install -r requirements.txt - pip install -e . - pytest tests/regression_tests/ \ No newline at end of file diff --git a/.github/workflows/unit_tests.yml b/.github/workflows/unit_tests.yml new file mode 100644 index 00000000..f29f0420 --- /dev/null +++ b/.github/workflows/unit_tests.yml @@ -0,0 +1,33 @@ +name: Unit Tests + +on: + pull_request: + branches: + - main + - dev + push: + branches: + - main + - dev + +jobs: + cpu-tests: + runs-on: ubuntu-latest + + steps: + - name: Check out repository + uses: actions/checkout@v4 + + - name: Set up Python + uses: actions/setup-python@v4 + with: + python-version: "3.10" + + - name: Install dependencies + run: | + python -m pip install --upgrade pip + pip install -r requirements.txt + + - name: Run pytest + run: | + pytest gym learning From c5a598f4b6c168cb7f1bcab3bd198d4f0e84575d Mon Sep 17 00:00:00 2001 From: sheim Date: Tue, 11 Nov 2025 10:30:22 -0500 Subject: [PATCH 44/77] change per-joint rewards to take the mean, to be more consistent across robots with different numbers of joints. Adjusted weights for mini_cheetah accordingly. --- gym/envs/base/legged_robot.py | 14 +++++++------- gym/envs/mini_cheetah/mini_cheetah_config.py | 6 +++--- gym/envs/mini_cheetah/mini_cheetah_osc_config.py | 6 +++--- gym/envs/mini_cheetah/mini_cheetah_ref_config.py | 6 +++--- 4 files changed, 16 insertions(+), 16 deletions(-) diff --git a/gym/envs/base/legged_robot.py b/gym/envs/base/legged_robot.py index 9923977d..d092dd5e 100644 --- a/gym/envs/base/legged_robot.py +++ b/gym/envs/base/legged_robot.py @@ -1010,11 +1010,11 @@ def _reward_base_height(self): def _reward_torques(self): """Penalize torques""" - return -torch.sum(torch.square(self.torques), dim=1) + return -torch.mean(torch.square(self.torques), dim=1) def _reward_dof_vel(self): """Penalize dof velocities""" - return -torch.sum(torch.square(self.dof_vel), dim=1) + return -torch.mean(torch.square(self.dof_vel), dim=1) def _reward_action_rate(self): """Penalize changes in actions""" @@ -1026,7 +1026,7 @@ def _reward_action_rate(self): ) / dt2 ) - return -torch.sum(error, dim=1) + return -torch.mean(error, dim=1) def _reward_action_rate2(self): """Penalize changes in actions""" @@ -1040,7 +1040,7 @@ def _reward_action_rate2(self): ) / dt2 ) - return -torch.sum(error, dim=1) + return -torch.mean(error, dim=1) def _reward_collision(self): """Penalize collisions on selected bodies""" @@ -1065,20 +1065,20 @@ def _reward_dof_pos_limits(self): # * lower limit out_of_limits = -(self.dof_pos - self.dof_pos_limits[:, 0]).clip(max=0.0) out_of_limits += (self.dof_pos - self.dof_pos_limits[:, 1]).clip(min=0.0) - return -torch.sum(out_of_limits, dim=1) + return -torch.mean(out_of_limits, dim=1) def _reward_dof_vel_limits(self): """Penalize dof velocities too close to the limit""" # * clip to max error = 1 rad/s per joint to avoid huge penalties limit = self.cfg.reward_settings.soft_dof_vel_limit error = self.dof_vel.abs() - self.dof_vel_limits * limit - return -torch.sum(error.clip(min=0.0, max=1.0), dim=1) + return -torch.mean(error.clip(min=0.0, max=1.0), dim=1) def _reward_torque_limits(self): """penalize torques too close to the limit""" limit = self.cfg.reward_settings.soft_torque_limit error = self.torques.abs() - self.torque_limits * limit - return -torch.sum(error.clip(min=0.0, max=1.0), dim=1) + return -torch.mean(error.clip(min=0.0, max=1.0), dim=1) def _reward_tracking_lin_vel(self): """Tracking of linear velocity commands (xy axes)""" diff --git a/gym/envs/mini_cheetah/mini_cheetah_config.py b/gym/envs/mini_cheetah/mini_cheetah_config.py index 5ec7ff18..beca88b7 100644 --- a/gym/envs/mini_cheetah/mini_cheetah_config.py +++ b/gym/envs/mini_cheetah/mini_cheetah_config.py @@ -175,11 +175,11 @@ class weights: lin_vel_z = 0.0 ang_vel_xy = 0.01 orientation = 1.0 - torques = 5.0e-7 + torques = 5.0e-6 dof_vel = 0.0 min_base_height = 1.5 - action_rate = 0.01 - action_rate2 = 0.001 + action_rate = 0.1 + action_rate2 = 0.01 stand_still = 0.0 dof_pos_limits = 0.0 feet_contact_forces = 0.0 diff --git a/gym/envs/mini_cheetah/mini_cheetah_osc_config.py b/gym/envs/mini_cheetah/mini_cheetah_osc_config.py index 50debce7..2da73256 100644 --- a/gym/envs/mini_cheetah/mini_cheetah_osc_config.py +++ b/gym/envs/mini_cheetah/mini_cheetah_osc_config.py @@ -240,12 +240,12 @@ class weights: lin_vel_z = 0.0 ang_vel_xy = 0.0 orientation = 1.0 - torques = 5.0e-7 + torques = 5.0e-6 dof_vel = 0.0 min_base_height = 1.0 collision = 0 - action_rate = 0.01 # -0.01 - action_rate2 = 0.001 # -0.001 + action_rate = 0.1 # -0.01 + action_rate2 = 0.01 # -0.001 stand_still = 0.0 dof_pos_limits = 0.0 feet_contact_forces = 0.0 diff --git a/gym/envs/mini_cheetah/mini_cheetah_ref_config.py b/gym/envs/mini_cheetah/mini_cheetah_ref_config.py index 74e60d59..596b8d9a 100644 --- a/gym/envs/mini_cheetah/mini_cheetah_ref_config.py +++ b/gym/envs/mini_cheetah/mini_cheetah_ref_config.py @@ -122,12 +122,12 @@ class weights: lin_vel_z = 0.0 ang_vel_xy = 0.01 orientation = 1.0 - torques = 5.0e-7 + torques = 5.0e-6 dof_vel = 0.0 min_base_height = 1.5 collision = 0.0 - action_rate = 0.01 - action_rate2 = 0.001 + action_rate = 0.1 + action_rate2 = 0.01 stand_still = 0.0 dof_pos_limits = 0.0 feet_contact_forces = 0.0 From 2a7d1a1c099ba47e67ccfd7c693a566a82d54b88 Mon Sep 17 00:00:00 2001 From: sheim Date: Wed, 10 Dec 2025 16:56:22 -0500 Subject: [PATCH 45/77] ruff --- .pre-commit-config.yaml | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index 075e1dbf..14539b8c 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -1,11 +1,11 @@ repos: - repo: https://github.com/astral-sh/ruff-pre-commit - rev: v0.1.3 + rev: v0.14.2 hooks: - id: ruff-format - repo: https://github.com/astral-sh/ruff-pre-commit - rev: v0.1.3 + rev: v0.14.2 hooks: - id: ruff From 8b9fcb137d9342acfd167f6451336b86ed01f3ad Mon Sep 17 00:00:00 2001 From: sheim Date: Wed, 10 Dec 2025 16:56:50 -0500 Subject: [PATCH 46/77] ruff --- learning/utils/logger/PerIterationLogs.py | 6 ++-- learning/utils/logger/test_logger.py | 36 +++++++++---------- .../test_runner_integration.py | 18 +++++----- .../test_generated_torch_files.py | 6 ++-- 4 files changed, 33 insertions(+), 33 deletions(-) diff --git a/learning/utils/logger/PerIterationLogs.py b/learning/utils/logger/PerIterationLogs.py index 0f30b899..5d781efb 100644 --- a/learning/utils/logger/PerIterationLogs.py +++ b/learning/utils/logger/PerIterationLogs.py @@ -6,9 +6,9 @@ def __init__(self): def register_items(self, category, target, attribute_list): if category in self.targets.keys(): - assert ( - self.targets[category] == target - ), "Category already registered with different target" + assert self.targets[category] == target, ( + "Category already registered with different target" + ) else: self.targets[category] = target self.logs[category] = {} diff --git a/learning/utils/logger/test_logger.py b/learning/utils/logger/test_logger.py index eaef76f6..941a4f0b 100644 --- a/learning/utils/logger/test_logger.py +++ b/learning/utils/logger/test_logger.py @@ -10,9 +10,9 @@ def __init__(self): def all_rewards_registered(logger, reward_names): for key in reward_names: - assert ( - key in logger.reward_logs.log_items.keys() - ), "key not registered in logger." + assert key in logger.reward_logs.log_items.keys(), ( + "key not registered in logger." + ) def only_rewards_registered(logger, reward_names): @@ -27,13 +27,13 @@ def only_category_registered(logger, categories): def both_target_and_log_set_up(logger): for key in logger.iteration_logs.logs.keys(): - assert ( - key in logger.iteration_logs.targets.keys() - ), "target not registered in PerIteration." + assert key in logger.iteration_logs.targets.keys(), ( + "target not registered in PerIteration." + ) for key in logger.iteration_logs.targets.keys(): - assert ( - key in logger.iteration_logs.logs.keys() - ), "log not registered in PerIteration." + assert key in logger.iteration_logs.logs.keys(), ( + "log not registered in PerIteration." + ) def test_logger_setup(): @@ -71,16 +71,16 @@ def check_episode_count(logger, expected_count=1): def check_average_time(logger, expected_time): avg_time = logger.reward_logs.get_average_time() - assert ( - abs(avg_time.item() - expected_time) < 1e-5 - ), f"Average time {avg_time} is not close to {expected_time}" + assert abs(avg_time.item() - expected_time) < 1e-5, ( + f"Average time {avg_time} is not close to {expected_time}" + ) def check_average_reward(logger, reward_name, expected_average): avg_reward = logger.reward_logs.get_average_rewards()[reward_name] - assert ( - abs(avg_reward.item() - expected_average) < 1e-5 - ), f"Average reward {avg_reward} is not close to {expected_average}" + assert abs(avg_reward.item() - expected_average) < 1e-5, ( + f"Average reward {avg_reward} is not close to {expected_average}" + ) def test_logging_rewards(): @@ -173,8 +173,8 @@ def test_timer(): ETA2 = logger.estimate_ETA(["first_step"], mode="total") expected_ETA2 = a * (1000 - 1) - assert ( - abs(ETA2 - expected_ETA2) < 1e-5 - ), f"ETA {ETA2} is not close to {expected_ETA2}" + assert abs(ETA2 - expected_ETA2) < 1e-5, ( + f"ETA {ETA2} is not close to {expected_ETA2}" + ) assert (a + b) >= 2 * trial_time, "Timer not working correctly." diff --git a/tests/integration_tests/test_runner_integration.py b/tests/integration_tests/test_runner_integration.py index 611108e5..d847f627 100644 --- a/tests/integration_tests/test_runner_integration.py +++ b/tests/integration_tests/test_runner_integration.py @@ -58,9 +58,9 @@ def test_default_integration_settings(self, args): with torch.no_grad(): actions = runner.get_inference_actions() deployed_actions = runner.env.get_states(runner.actor_cfg["actions"]) - assert ( - torch.equal(actions, torch.zeros_like(actions)) is False - ), "Policy returning all zeros" + assert torch.equal(actions, torch.zeros_like(actions)) is False, ( + "Policy returning all zeros" + ) assert ( torch.equal(deployed_actions, torch.zeros_like(deployed_actions)) is False ), "Actions not written to environment" @@ -78,9 +78,9 @@ def test_default_integration_settings(self, args): model_7_path = os.path.join(runner.log_dir, "model_7.pt") model_8_path = os.path.join(runner.log_dir, "model_8.pt") - assert os.path.exists( - model_0_path - ), f"{model_0_path} (pre-iteration) was not saved" + assert os.path.exists(model_0_path), ( + f"{model_0_path} (pre-iteration) was not saved" + ) assert not os.path.exists(model_1_path), f"{model_1_path} was saved" assert not os.path.exists(model_2_path), f"{model_2_path} was saved" assert os.path.exists(model_3_path), f"{model_3_path} was not saved" @@ -88,9 +88,9 @@ def test_default_integration_settings(self, args): assert not os.path.exists(model_5_path), f"{model_5_path} was saved" assert os.path.exists(model_6_path), f"{model_6_path} was not saved" assert not os.path.exists(model_7_path), f"{model_7_path} was saved" - assert os.path.exists( - model_8_path - ), f"{model_5_path}(last iteration) was not saved" + assert os.path.exists(model_8_path), ( + f"{model_5_path}(last iteration) was not saved" + ) obs = torch.randn_like(runner.get_obs(runner.actor_cfg["obs"])) actions_first = runner.alg.actor.act_inference(obs).cpu().clone() diff --git a/tests/regression_tests/test_generated_torch_files.py b/tests/regression_tests/test_generated_torch_files.py index cf640ed3..55626034 100644 --- a/tests/regression_tests/test_generated_torch_files.py +++ b/tests/regression_tests/test_generated_torch_files.py @@ -27,6 +27,6 @@ def test_generated_tensor_matches_reference(tmp_path): _generate_candidate_tensor(candidate_path) reference = _load_tensor(REFERENCE_FILE) candidate = _load_tensor(candidate_path) - assert torch.equal( - reference, candidate - ), f"Tensors in {REFERENCE_FILE} and {candidate_path} differ." + assert torch.equal(reference, candidate), ( + f"Tensors in {REFERENCE_FILE} and {candidate_path} differ." + ) From 18343284aed6a8370628680a1c167ab7747c0f4f Mon Sep 17 00:00:00 2001 From: sheim Date: Wed, 10 Dec 2025 18:05:50 -0500 Subject: [PATCH 47/77] catch sample history bug --- gym/envs/mit_humanoid/mit_humanoid.py | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/gym/envs/mit_humanoid/mit_humanoid.py b/gym/envs/mit_humanoid/mit_humanoid.py index a2e082e8..cb4db70f 100644 --- a/gym/envs/mit_humanoid/mit_humanoid.py +++ b/gym/envs/mit_humanoid/mit_humanoid.py @@ -84,8 +84,8 @@ def _reset_system(self, env_ids): def _reset_sampled_history_buffers(self, ids): n = self.cfg.env.sampled_history_length self.sampled_history_dof_pos_target[ids] = self.dof_pos_target[ids].tile(n) - self.sampled_history_dof_pos[ids] = self.dof_pos_target[ids].tile(n) - self.sampled_history_dof_vel[ids] = self.dof_pos_target[ids].tile(n) + self.sampled_history_dof_pos[ids] = self.dof_pos[ids].tile(n) + self.sampled_history_dof_vel[ids] = self.dof_vel[ids].tile(n) # compute_torques accounting for coupling, and filtering torques def _compute_torques(self): @@ -121,11 +121,11 @@ def _update_sampled_history_buffers(self): self.sampled_history_dof_pos[ids] = torch.roll( self.sampled_history_dof_pos[ids], self.num_dof, dims=1 ) # check - self.sampled_history_dof_pos[ids, : self.num_dof] = self.dof_pos_target[ids] + self.sampled_history_dof_pos[ids, : self.num_dof] = self.dof_pos[ids] self.sampled_history_dof_vel[ids] = torch.roll( self.sampled_history_dof_vel[ids], self.num_dof, dims=1 ) # check - self.sampled_history_dof_vel[ids, : self.num_dof] = self.dof_pos_target[ids] + self.sampled_history_dof_vel[ids, : self.num_dof] = self.dof_vel[ids] self.sampled_history_counter[ids] = 0 From b329655c65633bf8801d1a2aa0c7b561f1234177 Mon Sep 17 00:00:00 2001 From: sheim Date: Tue, 31 Mar 2026 16:17:00 -0400 Subject: [PATCH 48/77] remove old unused robots --- gym/envs/__init__.py | 8 - gym/envs/a1/a1.py | 60 - gym/envs/a1/a1_config.py | 223 --- 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resources/robots/cassie/urdf/cassie.urdf diff --git a/gym/envs/__init__.py b/gym/envs/__init__.py index 826b25e6..6787912b 100644 --- a/gym/envs/__init__.py +++ b/gym/envs/__init__.py @@ -16,8 +16,6 @@ "MiniCheetahRef": ".mini_cheetah.mini_cheetah_ref", "MiniCheetahOsc": ".mini_cheetah.mini_cheetah_osc", "MIT_Humanoid": ".mit_humanoid.mit_humanoid", - "Anymal": ".anymal_c.anymal", - "A1": ".a1.a1", "HumanoidRunning": ".mit_humanoid.humanoid_running", "Pendulum": ".pendulum.pendulum", "Lander": ".mit_humanoid.lander", @@ -30,8 +28,6 @@ "MiniCheetahOscCfg": ".mini_cheetah.mini_cheetah_osc_config", "MiniCheetahSACCfg": ".mini_cheetah.mini_cheetah_SAC_config", "MITHumanoidCfg": ".mit_humanoid.mit_humanoid_config", - "A1Cfg": ".a1.a1_config", - "AnymalCFlatCfg": ".anymal_c.flat.anymal_c_flat_config", "HumanoidRunningCfg": ".mit_humanoid.humanoid_running_config", "PendulumCfg": ".pendulum.pendulum_config", "PendulumSACCfg": ".pendulum.pendulum_SAC_config", @@ -46,8 +42,6 @@ "MiniCheetahOscRunnerCfg": ".mini_cheetah.mini_cheetah_osc_config", "MiniCheetahSACRunnerCfg": ".mini_cheetah.mini_cheetah_SAC_config", "MITHumanoidRunnerCfg": ".mit_humanoid.mit_humanoid_config", - "A1RunnerCfg": ".a1.a1_config", - "AnymalCFlatRunnerCfg": ".anymal_c.flat.anymal_c_flat_config", "HumanoidRunningRunnerCfg": ".mit_humanoid.humanoid_running_config", "PendulumRunnerCfg": ".pendulum.pendulum_config", "PendulumSACRunnerCfg": ".pendulum.pendulum_SAC_config", @@ -79,8 +73,6 @@ "HumanoidRunningCfg", "HumanoidRunningRunnerCfg", ], - "flat_anymal_c": ["Anymal", "AnymalCFlatCfg", "AnymalCFlatRunnerCfg"], - "pendulum": ["Pendulum", "PendulumCfg", "PendulumRunnerCfg"], "sac_pendulum": ["Pendulum", "PendulumSACCfg", "PendulumSACRunnerCfg"], "lander": ["Lander", "LanderCfg", "LanderRunnerCfg"], "psd_pendulum": ["Pendulum", "PendulumPSDCfg", "PendulumPSDRunnerCfg"], diff --git a/gym/envs/a1/a1.py b/gym/envs/a1/a1.py deleted file mode 100644 index cbd5999c..00000000 --- a/gym/envs/a1/a1.py +++ /dev/null @@ -1,60 +0,0 @@ -import torch - -from gym.envs.base.legged_robot import LeggedRobot - - -class A1(LeggedRobot): - def __init__(self, gym, sim, cfg, sim_params, sim_device, headless): - super().__init__(gym, sim, cfg, sim_params, sim_device, headless) - - def _init_buffers(self): - super()._init_buffers() - self.dof_pos_obs = torch.zeros_like(self.dof_pos, requires_grad=False) - self.base_height = torch.zeros( - self.num_envs, 1, device=self.device, requires_grad=False - ) - - def _reward_lin_vel_z(self): - """Penalize z axis base linear velocity w. squared exp""" - return self._sqrdexp(self.base_lin_vel[:, 2] / self.scales["base_lin_vel"]) - - def _reward_ang_vel_xy(self): - """Penalize xy axes base angular velocity""" - error = self._sqrdexp(self.base_ang_vel[:, :2] / self.scales["base_ang_vel"]) - return torch.sum(error, dim=1) - - def _reward_orientation(self): - """Penalize non flat base orientation""" - error = ( - torch.square(self.projected_gravity[:, :2]) - / self.cfg.reward_settings.tracking_sigma - ) - return torch.sum(torch.exp(-error), dim=1) - - def _reward_min_base_height(self): - """Squared exponential saturating at base_height target""" - base_height = self.root_states[:, 2].unsqueeze(1) - error = base_height - self.cfg.reward_settings.base_height_target - error /= self.scales["base_height"] - error = torch.clamp(error, max=0, min=None).flatten() - return torch.exp(-torch.square(error) / self.cfg.reward_settings.tracking_sigma) - - def _reward_tracking_lin_vel(self): - """Tracking of linear velocity commands (xy axes)""" - error = self.commands[:, :2] - self.base_lin_vel[:, :2] - # * scale by (1+|cmd|): if cmd=0, no scaling. - error *= 1 / (1 + torch.abs(self.commands[:, :2])) - error = torch.sum(torch.square(error), dim=1) - return torch.exp(-error / self.cfg.reward_settings.tracking_sigma) - - def _reward_dof_vel(self): - """Penalize dof velocities""" - return torch.sum(self._sqrdexp(self.dof_vel / self.scales["dof_vel"]), dim=1) - - def _reward_dof_near_home(self): - return torch.sum( - self._sqrdexp( - (self.dof_pos - self.default_dof_pos) / self.scales["dof_pos_obs"] - ), - dim=1, - ) diff --git a/gym/envs/a1/a1_config.py b/gym/envs/a1/a1_config.py deleted file mode 100644 index 87477b56..00000000 --- a/gym/envs/a1/a1_config.py +++ /dev/null @@ -1,223 +0,0 @@ -from gym.envs.base.legged_robot_config import ( - LeggedRobotCfg, - LeggedRobotRunnerCfg, -) - -BASE_HEIGHT_REF = 0.33 - - -class A1Cfg(LeggedRobotCfg): - class env(LeggedRobotCfg.env): - num_envs = 2**12 - num_actuators = 12 - episode_length_s = 5.0 - - class terrain(LeggedRobotCfg.terrain): - mesh_type = "plane" - - class init_state(LeggedRobotCfg.init_state): - default_joint_angles = { # = target angles [rad] when action = 0.0 - "FL_hip_joint": 0.1, # [rad] - "RL_hip_joint": 0.1, # [rad] - "FR_hip_joint": -0.1, # [rad] - "RR_hip_joint": -0.1, # [rad] - "FL_thigh_joint": 0.8, # [rad] - "RL_thigh_joint": 1.0, # [rad] - "FR_thigh_joint": 0.8, # [rad] - "RR_thigh_joint": 1.0, # [rad] - "FL_calf_joint": -1.5, # [rad] - "RL_calf_joint": -1.5, # [rad] - "FR_calf_joint": -1.5, # [rad] - "RR_calf_joint": -1.5, # [rad] - } - - reset_mode = "reset_to_basic" - # reset setup chooses how the initial conditions are chosen. - # "reset_to_basic" = a single position - # "reset_to_range" = uniformly random from a range defined below - - # * default COM for basic initialization - pos = [0.0, 0.0, 0.33] # x,y,z [m] - rot = [0.0, 0.0, 0.0, 1.0] # x,y,z,w [quat] - lin_vel = [0.0, 0.0, 0.0] # x,y,z [m/s] - ang_vel = [0.0, 0.0, 0.0] # x,y,z [rad/s] - - # * initialization for random range setup - - dof_pos_range = { - "haa": [-0.05, 0.05], - "hfe": [-0.85, -0.6], - "kfe": [-1.45, 1.72], - } - - dof_vel_range = { - "haa": [0.0, 0.0], - "hfe": [0.0, 0.0], - "kfe": [0.0, 0.0], - } - - root_pos_range = [ - [0.0, 0.0], # x - [0.0, 0.0], # y - [0.37, 0.4], # z - [0.0, 0.0], # roll - [0.0, 0.0], # pitch - [0.0, 0.0], - ] # yaw - root_vel_range = [ - [-0.05, 0.05], # x - [0.0, 0.0], # y - [-0.05, 0.05], # z - [0.0, 0.0], # roll - [0.0, 0.0], # pitch - [0.0, 0.0], - ] # yaw - - class control(LeggedRobotCfg.control): - # PD Drive parameters: - stiffness = {"joint": 20.0} # [N*m/rad] - damping = {"joint": 0.5} # [N*m*s/rad] - - ctrl_frequency = 100 - desired_sim_frequency = 500 - - class commands: - resampling_time = 10.0 # time before command are changed[s] - - class ranges: - lin_vel_x = [-1.0, 1.0] # min max [m/s] - lin_vel_y = 1.0 # max [m/s] - yaw_vel = 1 # max [rad/s] - - class push_robots: - toggle = True - interval_s = 1 - max_push_vel_xy = 0.5 - - class domain_rand: - randomize_friction = False - friction_range = [0.5, 1.25] - randomize_base_mass = False - added_mass_range = [-1.0, 1.0] - - class asset(LeggedRobotCfg.asset): - file = "{LEGGED_GYM_ROOT_DIR}/resources/robots/a1/urdf/a1.urdf" - foot_name = "foot" - penalize_contacts_on = [] - terminate_after_contacts_on = ["base", "thigh", "hip"] - # merge bodies connected by fixed joints. - collapse_fixed_joints = False - # added blindly from the AnymalCFlatCFG. - # 1 to disable, 0 to enable...bitwise filter - self_collisions = 1 - flip_visual_attachments = True - disable_gravity = False - disable_motors = False # all torques set to 0 - - class reward_settings(LeggedRobotCfg.reward_settings): - soft_dof_pos_limit = 0.9 - soft_dof_vel_limit = 0.9 - soft_torque_limit = 0.9 - max_contact_force = 600.0 - base_height_target = BASE_HEIGHT_REF - tracking_sigma = 0.25 - - class scaling(LeggedRobotCfg.scaling): - base_ang_vel = 3.14 * (BASE_HEIGHT_REF / 9.81) ** 0.5 - base_lin_vel = 1.0 - commands = 1 - dof_vel = 100.0 # ought to be roughly max expected speed. - base_height = BASE_HEIGHT_REF - dof_pos = 4 * [0.1, 1.0, 2] # hip-abad, hip-pitch, knee - dof_pos_obs = dof_pos - # Action scales - dof_pos_target = dof_pos - tau_ff = 4 * [18, 18, 28] # hip-abad, hip-pitch, knee - - -class A1RunnerCfg(LeggedRobotRunnerCfg): - seed = -1 - runner_class_name = "OldPolicyRunner" - - class actor(LeggedRobotRunnerCfg.actor): - hidden_dims = [256, 256, 256] - # activation can be elu, relu, selu, crelu, lrelu, tanh, sigmoid - activation = "elu" - obs = [ - "base_height", - "base_lin_vel", - "base_ang_vel", - "projected_gravity", - "dof_pos_obs", - "dof_vel", - "dof_pos_history", - "commands", - ] - - actions = ["dof_pos_target"] - - class noise: - dof_pos_obs = 0.005 - dof_vel = 0.005 - base_ang_vel = 0.05 - projected_gravity = 0.02 - - class critic: - hidden_dims = [256, 256, 256] - # activation can be elu, relu, selu, crelu, lrelu, tanh, sigmoid - activation = "elu" - obs = [ - "base_height", - "base_lin_vel", - "base_ang_vel", - "projected_gravity", - "dof_pos_obs", - "dof_vel", - "dof_pos_history", - "commands", - ] - - class reward: - class weights: - tracking_lin_vel = 1.0 - tracking_ang_vel = 1.0 - lin_vel_z = 0.0 - ang_vel_xy = 0.0 - orientation = 1.0 - torques = 5.0e-7 - dof_vel = 0.0 - base_height = 1.0 - action_rate = 0.001 - action_rate2 = 0.0001 - stand_still = 0.0 - dof_pos_limits = 0.0 - feet_contact_forces = 0.0 - dof_near_home = 1.0 - - class termination_weight: - termination = 1.0 - - class algorithm(LeggedRobotRunnerCfg.algorithm): - # training params - value_loss_coef = 1.0 - use_clipped_value_loss = True - clip_param = 0.2 - entropy_coef = 0.01 - num_learning_epochs = 5 - # mini batch size = num_envs*nsteps / nminibatches - num_mini_batches = 4 - learning_rate = 1.0e-3 # 5.e-4 - schedule = "adaptive" # could be adaptive, fixed - discount_horizon = 0.5 # [s] - GAE_bootstrap_horizon = 0.2 # [s] - desired_kl = 0.01 - max_grad_norm = 1.0 - - class runner(LeggedRobotRunnerCfg.runner): - run_name = "" - experiment_name = "a1" - max_iterations = 500 # number of policy updates - algorithm_class_name = "PPO" - # per iteration - # (n_steps in Rudin 2021 paper - batch_size = n_steps * n_robots) - num_steps_per_env = 24 diff --git a/gym/envs/anymal_b/anymal_b_config.py b/gym/envs/anymal_b/anymal_b_config.py deleted file mode 100644 index 25189c00..00000000 --- a/gym/envs/anymal_b/anymal_b_config.py +++ /dev/null @@ -1,52 +0,0 @@ -# SPDX-FileCopyrightText: Copyright (c) 2021 NVIDIA CORPORATION & AFFILIATES. -# All rights reserved. -# SPDX-License-Identifier: BSD-3-Clause -# -# Redistribution and use in source and binary forms, with or without -# modification, are permitted provided that the following conditions are met: -# -# 1. Redistributions of source code must retain the above copyright notice, -# this list of conditions and the following disclaimer. -# -# 2. Redistributions in binary form must reproduce the above copyright notice, -# this list of conditions and the following disclaimer in the documentation -# and/or other materials provided with the distribution. -# -# 3. Neither the name of the copyright holder nor the names of its -# contributors may be used to endorse or promote products derived from -# this software without specific prior written permission. -# -# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" -# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE -# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE -# ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE -# ARE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR -# CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF -# SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS -# INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN -# CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) -# ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF -# THE POSSIBILITY OF SUCH DAMAGE. -# -# Copyright (c) 2021 ETH Zurich, Nikita Rudin - -from gym.envs import AnymalCRoughCfg, AnymalCRoughCfgPPO - -PATH_TO_URDFS = "/resources/robots/anymal_b/urdf" - - -class AnymalBRoughCfg(AnymalCRoughCfg): - class asset(AnymalCRoughCfg.asset): - file = "{LEGGED_GYM_ROOT_DIR}{PATH_TO_URDFS}/anymal_b.urdf" - foot_name = "FOOT" - - class rewards(AnymalCRoughCfg.rewards): - class weights(AnymalCRoughCfg.rewards.weights): - pass - - -class AnymalBRoughCfgPPO(AnymalCRoughCfgPPO): - class runner(AnymalCRoughCfgPPO.runner): - run_name = "" - experiment_name = "rough_anymal_b" - load_run = -1 diff --git a/gym/envs/anymal_c/anymal.py b/gym/envs/anymal_c/anymal.py deleted file mode 100644 index c5a311a6..00000000 --- a/gym/envs/anymal_c/anymal.py +++ /dev/null @@ -1,170 +0,0 @@ -# SPDX-FileCopyrightText: Copyright (c) 2021 NVIDIA CORPORATION & AFFILIATES. -# All rights reserved. -# SPDX-License-Identifier: BSD-3-Clause -# -# Redistribution and use in source and binary forms, with or without -# modification, are permitted provided that the following conditions are met: -# -# 1. Redistributions of source code must retain the above copyright notice, -# this list of conditions and the following disclaimer. -# -# 2. Redistributions in binary form must reproduce the above copyright notice, -# this list of conditions and the following disclaimer in the documentation -# and/or other materials provided with the distribution. -# -# 3. Neither the name of the copyright holder nor the names of its -# contributors may be used to endorse or promote products derived from -# this software without specific prior written permission. -# -# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" -# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE -# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE -# ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE -# ARE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR -# CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF -# SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS -# INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN -# CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) -# ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF -# THE POSSIBILITY OF SUCH DAMAGE. -# -# Copyright (c) 2021 ETH Zurich, Nikita Rudin - -import torch - -from gym.envs.base.legged_robot import LeggedRobot -from gym import LEGGED_GYM_ROOT_DIR - - -class Anymal(LeggedRobot): - def __init__(self, gym, sim, cfg, sim_params, sim_device, headless): - super().__init__(gym, sim, cfg, sim_params, sim_device, headless) - - # load actuator network - if self.cfg.control.use_actuator_network: - net_file = self.cfg.control.actuator_net_file - sea_network_path = net_file.format(LEGGED_GYM_ROOT_DIR=LEGGED_GYM_ROOT_DIR) - self.actuator_network = torch.jit.load(sea_network_path).to(self.device) - - def reset_idx(self, env_ids): - super().reset_idx(env_ids) - # Additionaly empty actuator network hidden states - self.sea_hidden_state_per_env[:, env_ids] = 0.0 - self.sea_cell_state_per_env[:, env_ids] = 0.0 - - def _init_buffers(self): - super()._init_buffers() - # Additionally initialize actuator network hidden state tensors - if self.cfg.control.use_actuator_network: - self._init_sea() - - def _init_sea(self): - num_sea_states = self.num_envs * self.num_actuators - self.sea_input = torch.zeros(num_sea_states, 1, 2, device=self.device) - self.sea_hidden_state = torch.zeros(2, num_sea_states, 8, device=self.device) - self.sea_cell_state = torch.zeros(2, num_sea_states, 8, device=self.device) - self.sea_hidden_state_per_env = self.sea_hidden_state.view( - 2, self.num_envs, self.num_actuators, 8 - ) - self.sea_cell_state_per_env = self.sea_cell_state.view( - 2, self.num_envs, self.num_actuators, 8 - ) - - def _compute_torques(self): - if self.cfg.control.use_actuator_network: - with torch.inference_mode(): - self.sea_input[:, 0, 0] = ( - self.cfg.actions * self.cfg.control.action_scale - + self.default_dof_pos - - self.dof_pos - ).flatten() - self.sea_input[:, 0, 1] = self.dof_vel.flatten() - ( - torques, - (self.sea_hidden_state[:], self.sea_cell_state[:]), - ) = self.actuator_network( - self.sea_input, - (self.sea_hidden_state, self.sea_cell_state), - ) - return torques - else: - return super()._compute_torques() - - def _switch(self): - c_vel = torch.linalg.norm(self.commands, dim=1) - return torch.exp( - -torch.square(torch.max(torch.zeros_like(c_vel), c_vel - 0.2)) / 0.1 - ) - - def _reward_lin_vel_z(self): - """ - Penalize z axis base linear velocity w. squared exp - """ - - return self._sqrdexp(self.base_lin_vel[:, 2] / self.scales["base_lin_vel"]) - - def _reward_ang_vel_xy(self): - """ - Penalize xy axes base angular velocity - """ - error = self._sqrdexp(self.base_ang_vel[:, :2] / self.scales["base_ang_vel"]) - return torch.sum(error, dim=1) - - def _reward_orientation(self): - """ - Penalize non flat base orientation - """ - error = ( - torch.square(self.projected_gravity[:, :2]) - / self.cfg.reward_settings.tracking_sigma - ) - return torch.sum(torch.exp(-error), dim=1) - - def _reward_min_base_height(self): - """ - Squared exponential saturating at base_height target - """ - base_height = self.root_states[:, 2].unsqueeze(1) - error = base_height - self.cfg.reward_settings.base_height_target - error /= self.scales["base_height"] - error = torch.clamp(error, max=0, min=None).flatten() - return torch.exp(-torch.square(error) / self.cfg.reward_settings.tracking_sigma) - - def _reward_tracking_lin_vel(self): - """ - Tracking of linear velocity commands (xy axes) - """ - - error = self.commands[:, :2] - self.base_lin_vel[:, :2] - # * scale by (1+|cmd|): if cmd=0, no scaling. - error *= 1.0 / (1.0 + torch.abs(self.commands[:, :2])) - error = torch.sum(torch.square(error), dim=1) - return torch.exp(-error / self.cfg.reward_settings.tracking_sigma) - - def _reward_dof_vel(self): - """ - Penalize dof velocities - """ - - return torch.mean(self._sqrdexp(self.dof_vel / self.scales["dof_vel"]), dim=1) - - def _reward_stand_still(self): - """ - Penalize motion at zero commands - """ - - # * normalize angles so we care about being within 5 deg - error = (self.dof_pos - self.default_dof_pos) / torch.pi * 36 - rew_pos = torch.mean(self._sqrdexp(error), dim=1) - rew_vel = torch.mean(self._sqrdexp(self.dof_vel), dim=1) - rew_base_vel = torch.mean(torch.square(self.base_lin_vel), dim=1) - rew_base_vel += torch.mean(torch.square(self.base_ang_vel), dim=1) - return (rew_vel + rew_pos - rew_base_vel) * self._switch() - - def _reward_dof_near_home(self): - return torch.mean( - self._sqrdexp( - (self.dof_pos - self.default_dof_pos) / self.scales["dof_pos_obs"] - ), - dim=1, - ) diff --git a/gym/envs/anymal_c/flat/anymal_c_flat_config.py b/gym/envs/anymal_c/flat/anymal_c_flat_config.py deleted file mode 100644 index d18f7cca..00000000 --- a/gym/envs/anymal_c/flat/anymal_c_flat_config.py +++ /dev/null @@ -1,206 +0,0 @@ -# SPDX-FileCopyrightText: Copyright (c) 2021 NVIDIA CORPORATION & AFFILIATES. -# All rights reserved. -# SPDX-License-Identifier: BSD-3-Clause -# -# Redistribution and use in source and binary forms, with or without -# modification, are permitted provided that the following conditions are met: -# -# 1. Redistributions of source code must retain the above copyright notice, -# this list of conditions and the following disclaimer. -# -# 2. Redistributions in binary form must reproduce the above copyright notice, -# this list of conditions and the following disclaimer in the documentation -# and/or other materials provided with the distribution. -# -# 3. Neither the name of the copyright holder nor the names of its -# contributors may be used to endorse or promote products derived from -# this software without specific prior written permission. -# -# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" -# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE -# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE -# ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE -# LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR -# CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF -# SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS -# INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN -# CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) -# ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF -# THE POSSIBILITY OF SUCH DAMAGE. -# -# Copyright (c) 2021 ETH Zurich, Nikita Rudin - -from gym.envs.base.legged_robot_config import ( - LeggedRobotCfg, - LeggedRobotRunnerCfg, -) - -BASE_HEIGHT_REF = 0.5 - - -class AnymalCFlatCfg(LeggedRobotCfg): - class env(LeggedRobotCfg.env): - num_envs = 4096 - num_actions = 12 - - class terrain(LeggedRobotCfg.terrain): - mesh_type = "plane" - - class init_state(LeggedRobotCfg.init_state): - pos = [0.0, 0.0, 0.6] # x,y,z [m] - default_joint_angles = { # = target angles [rad] when action = 0.0 - "LF_HAA": 0.0, - "LH_HAA": 0.0, - "RF_HAA": -0.0, - "RH_HAA": -0.0, - "LF_HFE": 0.4, - "LH_HFE": -0.4, - "RF_HFE": 0.4, - "RH_HFE": -0.4, - "LF_KFE": -0.8, - "LH_KFE": 0.8, - "RF_KFE": -0.8, - "RH_KFE": 0.8, - } - - class control(LeggedRobotCfg.control): - stiffness = {"HAA": 80.0, "HFE": 80.0, "KFE": 80.0} # [N*m/rad] - damping = {"HAA": 2.0, "HFE": 2.0, "KFE": 2.0} # [N*m*s/rad] - use_actuator_network = False - actuator_net_file = ( - "{LEGGED_GYM_ROOT_DIR}/resources/" + "actuator_nets/anydrive_v3_lstm.pt" - ) - ctrl_frequency = 80 - desired_sim_frequency = 400 - - class commands: - resampling_time = 10.0 # time before command are changed[s] - - class ranges: - lin_vel_x = [-1.0, 3.0] # min max [m/s] - lin_vel_y = 1.0 # max [m/s] - yaw_vel = 2 # max [rad/s] - - class push_robots: - toggle = True - interval_s = 1 - max_push_vel_xy = 0.5 - push_box_dims = [0.2, 0.2, 0.2] - - class domain_rand(LeggedRobotCfg.domain_rand): - randomize_base_mass = True - added_mass_range = [-2.0, 2.0] - - class asset(LeggedRobotCfg.asset): - file = "{LEGGED_GYM_ROOT_DIR}/resources/robots/" + "anymal_c/urdf/anymal_c.urdf" - foot_name = "FOOT" - penalize_contacts_on = ["SHANK"] - terminate_after_contacts_on = ["BASE", "THIGH"] - self_collisions = 0 # 1 to disable, 0 to enable...bitwise filter - - class reward_settings(LeggedRobotCfg.reward_settings): - soft_dof_pos_limit = 0.9 - soft_dof_vel_limit = 0.9 - soft_torque_limit = 0.9 - max_contact_force = 600.0 - base_height_target = BASE_HEIGHT_REF - tracking_sigma = 0.25 - - class scaling(LeggedRobotCfg.scaling): - base_ang_vel = 3.14 * (BASE_HEIGHT_REF / 9.81) ** 0.5 - base_lin_vel = 1.0 - commands = 1 - dof_vel = 100.0 # ought to be roughly max expected speed. - base_height = BASE_HEIGHT_REF - dof_pos = 4 * [0.1, 1.0, 2] # hip-abad, hip-pitch, knee - dof_pos_obs = dof_pos - # Action scales - dof_pos_target = dof_pos - tau_ff = 4 * [80, 80, 80] # hip-abad, hip-pitch, knee - - -class AnymalCFlatRunnerCfg(LeggedRobotRunnerCfg): - seed = -1 - runner_class_name = "OldPolicyRunner" - - class actor(LeggedRobotRunnerCfg.actor): - hidden_dims = [256, 256, 256] - # can be elu, relu, selu, crelu, lrelu, tanh, sigmoid - activation = "elu" - - obs = [ - "base_height", - "base_lin_vel", - "base_ang_vel", - "projected_gravity", - "commands", - "dof_pos_obs", - "dof_vel", - "dof_pos_history", - ] - - actions = ["dof_pos_target"] - - class noise: - dof_pos_obs = 0.005 - dof_vel = 0.005 - base_ang_vel = 0.05 # 0.027, 0.14, 0.37 - projected_gravity = 0.02 - - class critic(LeggedRobotRunnerCfg.critic): - hidden_dims = [256, 256, 256] - # can be elu, relu, selu, crelu, lrelu, tanh, sigmoid - activation = "elu" - obs = [ - "base_height", - "base_lin_vel", - "base_ang_vel", - "projected_gravity", - "commands", - "dof_pos_obs", - "dof_vel", - "dof_pos_history", - ] - - class reward: - class weights: - tracking_lin_vel = 3.0 - tracking_ang_vel = 1.0 - lin_vel_z = 0.0 - ang_vel_xy = 0.0 - orientation = 1.0 - torques = 5.0e-6 - dof_vel = 1.0 - base_height = 0.2 - action_rate = 0.001 # -0.01 - action_rate2 = 0.0001 # -0.001 - stand_still = 0.5 - dof_pos_limits = 0.0 - feet_contact_forces = 0.0 - dof_near_home = 0.1 - - class termination_weight: - termination = 1.0 - - class algorithm(LeggedRobotRunnerCfg.algorithm): - # training params, set to be same as mini_cheetah for right now - value_loss_coef = 1.0 - use_clipped_value_loss = True - clip_param = 0.2 - entropy_coef = 0.01 - num_learning_epochs = 5 - # mini batch size = num_envs*nsteps / nminibatches - num_mini_batches = 4 - learning_rate = 1.0e-3 # 5.e-4 - schedule = "adaptive" # could be adaptive, fixed - discount_horizon = 2.0 # [s] - GAE_bootstrap_horizon = 0.5 # [s] - desired_kl = 0.01 - max_grad_norm = 1.0 - - class runner(LeggedRobotRunnerCfg.runner): - run_name = "" - experiment_name = "flat_anymal_c" - algorithm_class_name = "PPO" - max_iterations = 1000 # number of policy updates - num_steps_per_env = 24 diff --git a/gym/envs/anymal_c/mixed_terrains/anymal_c_rough_config.py b/gym/envs/anymal_c/mixed_terrains/anymal_c_rough_config.py deleted file mode 100644 index 85b0c42b..00000000 --- a/gym/envs/anymal_c/mixed_terrains/anymal_c_rough_config.py +++ /dev/null @@ -1,75 +0,0 @@ -# SPDX-FileCopyrightText: Copyright (c) 2021 NVIDIA CORPORATION & AFFILIATES. -# All rights reserved. -# SPDX-License-Identifier: BSD-3-Clause -# -# Redistribution and use in source and binary forms, with or without -# modification, are permitted provided that the following conditions are met: -# -# 1. Redistributions of source code must retain the above copyright notice, -# this list of conditions and the following disclaimer. -# -# 2. Redistributions in binary form must reproduce the above copyright notice, -# this list of conditions and the following disclaimer in the documentation -# and/or other materials provided with the distribution. -# -# 3. Neither the name of the copyright holder nor the names of its -# contributors may be used to endorse or promote products derived from -# this software without specific prior written permission. -# -# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" -# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE -# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE -# ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE -# ARE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR -# CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF -# SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS -# INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN -# CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) -# ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF -# THE POSSIBILITY OF SUCH DAMAGE. -# -# Copyright (c) 2021 ETH Zurich, Nikita Rudin - -from gym.envs import AnymalCFlatCfg, AnymalCFlatCfgPPO - - -class AnymalCRoughCfg(AnymalCFlatCfg): - class env(AnymalCFlatCfg.env): - num_observations = 48 - - class terrain(AnymalCFlatCfg.terrain): - mesh_type = "trimesh" - measure_heights = False - - class asset(AnymalCFlatCfg.asset): - # 1 to disable, 0 to enable...bitwise filter - self_collisions = 1 - - class commands(AnymalCFlatCfg.commands): - resampling_time = 4.0 - - class ranges(AnymalCFlatCfg.commands.ranges): - yaw_vel = 1.5 - - class domain_rand(AnymalCFlatCfg.domain_rand): - friction_range = [0.0, 1.5] - - -class AnymalCRoughCCfgPPO(AnymalCFlatCfgPPO): - class actor(AnymalCFlatCfgPPO.actor): - hidden_dims = [128, 64, 32] - critic_hidden_dims = [128, 64, 32] - # can be elu, relu, selu, crelu, lrelu, tanh, sigmoid - activation = "elu" - - class critic(AnymalCFlatCfgPPO.critic): - pass - - class algorithm(AnymalCFlatCfgPPO.algorithm): - entropy_coef = 0.01 - - class runner(AnymalCFlatCfgPPO.runner): - run_name = "" - experiment_name = "rough_anymal_c" - load_run = -1 - max_iterations = 300 diff --git a/gym/envs/cassie/cassie.py b/gym/envs/cassie/cassie.py deleted file mode 100644 index 4c9eb1fb..00000000 --- a/gym/envs/cassie/cassie.py +++ /dev/null @@ -1,42 +0,0 @@ -# SPDX-FileCopyrightText: Copyright (c) 2021 NVIDIA CORPORATION & AFFILIATES. -# All rights reserved. -# SPDX-License-Identifier: BSD-3-Clause -# -# Redistribution and use in source and binary forms, with or without -# modification, are permitted provided that the following conditions are met: -# -# 1. Redistributions of source code must retain the above copyright notice, -# this list of conditions and the following disclaimer. -# -# 2. Redistributions in binary form must reproduce the above copyright notice, -# this list of conditions and the following disclaimer in the documentation -# and/or other materials provided with the distribution. -# -# 3. Neither the name of the copyright holder nor the names of its -# contributors may be used to endorse or promote products derived from -# this software without specific prior written permission. -# -# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" -# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE -# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE -# ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE -# LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR -# CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF -# SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS -# INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN -# CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) -# ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF -# THE POSSIBILITY OF SUCH DAMAGE. -# -# Copyright (c) 2021 ETH Zurich, Nikita Rudin - - -import torch -from gym.envs import LeggedRobot - - -class Cassie(LeggedRobot): - def _reward_no_fly(self): - contacts = self.contact_forces[:, self.feet_indices, 2] > 0.1 - single_contact = torch.sum(1.0 * contacts, dim=1) == 1 - return 1.0 * single_contact diff --git a/gym/envs/cassie/cassie_config.py b/gym/envs/cassie/cassie_config.py deleted file mode 100644 index c4dffb0f..00000000 --- a/gym/envs/cassie/cassie_config.py +++ /dev/null @@ -1,116 +0,0 @@ -# SPDX-FileCopyrightText: Copyright (c) 2021 NVIDIA CORPORATION & AFFILIATES. -# All rights reserved. -# SPDX-License-Identifier: BSD-3-Clause -# -# Redistribution and use in source and binary forms, with or without -# modification, are permitted provided that the following conditions are met: -# -# 1. Redistributions of source code must retain the above copyright notice, -# this list of conditions and the following disclaimer. -# -# 2. Redistributions in binary form must reproduce the above copyright notice, -# this list of conditions and the following disclaimer in the documentation -# and/or other materials provided with the distribution. -# -# 3. Neither the name of the copyright holder nor the names of its -# contributors may be used to endorse or promote products derived from -# this software without specific prior written permission. -# -# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" -# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE -# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE -# ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE -# ARE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR -# CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF -# SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS -# INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN -# CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) -# ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF -# THE POSSIBILITY OF SUCH DAMAGE. -# -# Copyright (c) 2021 ETH Zurich, Nikita Rudin - - -from gym.envs.base.legged_robot_config import LeggedRobotCfg, LeggedRobotCfgPPO - - -class CassieRoughCfg(LeggedRobotCfg): - class env(LeggedRobotCfg.env): - num_envs = 4096 - num_observations = 48 # 169 - num_actions = 12 - - class terrain(LeggedRobotCfg.terrain): - mesh_type = "plane" - measure_heights = False - - class init_state(LeggedRobotCfg.init_state): - pos = [0.0, 0.0, 1.5] # x,y,z [m] - default_joint_angles = { # = target angles [rad] when action = 0.0 - "hip_abduction_left": 0.2, # -0.3927 : 0.2618 - "hip_rotation_left": 0.3, # -0.3927 : 0.3927" - "hip_flexion_left": 1.0, # -0.8727 : 1.3963 - "thigh_joint_left": -0.8, # -2.8623 : -0.6458 - "ankle_joint_left": 1.57, # 0.6458 : 2.8623 - "toe_joint_left": -0.57, # -2.4435 : -0.5236 - "hip_abduction_right": -0.3, - "hip_rotation_right": -0.25, - "hip_flexion_right": 1.0, - "thigh_joint_right": -2.8, - "ankle_joint_right": 1.57, - "toe_joint_right": -2.17, - } - - class control(LeggedRobotCfg.control): - # PD Drive parameters: - stiffness = { - "hip_abduction": 100.0, - "hip_rotation": 100.0, - "hip_flexion": 200.0, - "thigh_joint": 200.0, - "ankle_joint": 200.0, - "toe_joint": 40.0, - } # [N*m/rad] - damping = { - "hip_abduction": 3.0, - "hip_rotation": 3.0, - "hip_flexion": 6.0, - "thigh_joint": 6.0, - "ankle_joint": 6.0, - "toe_joint": 1.0, - } # [N*m*s/rad] # [N*m*s/rad] - - class asset(LeggedRobotCfg.asset): - file = "{LEGGED_GYM_ROOT_DIR}/resources/robots/cassie/urdf/cassie.urdf" - foot_name = "toe" - terminate_after_contacts_on = ["pelvis"] - flip_visual_attachments = False - disable_gravity = True - self_collisions = 1 - - class rewards(LeggedRobotCfg.rewards): - soft_dof_pos_limit = 0.95 - soft_dof_vel_limit = 0.9 - soft_torque_limit = 0.9 - max_contact_force = 300.0 - - class weights(LeggedRobotCfg.rewards.weights): - termination = -200.0 - tracking_ang_vel = 1.0 - torques = -5.0e-6 - lin_vel_z = -0.5 - feet_air_time = 5.0 - dof_pos_limits = -1.0 - no_fly = 0.25 - dof_vel = -0.0 - ang_vel_xy = -0.0 - feet_contact_forces = -0.0 - - -class CassieRoughCfgPPO(LeggedRobotCfgPPO): - class runner(LeggedRobotCfgPPO.runner): - run_name = "" - experiment_name = "rough_cassie" - - class algorithm(LeggedRobotCfgPPO.algorithm): - entropy_coef = 0.01 diff --git a/resources/actuator_nets/anydrive_v3_lstm.pt b/resources/actuator_nets/anydrive_v3_lstm.pt deleted file mode 100644 index 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Definitions --------------- - -1.1. "Contributor" - means each individual or legal entity that creates, contributes to - the creation of, or owns Covered Software. - -1.2. "Contributor Version" - means the combination of the Contributions of others (if any) used - by a Contributor and that particular Contributor's Contribution. - -1.3. "Contribution" - means Covered Software of a particular Contributor. - -1.4. "Covered Software" - means Source Code Form to which the initial Contributor has attached - the notice in Exhibit A, the Executable Form of such Source Code - Form, and Modifications of such Source Code Form, in each case - including portions thereof. - -1.5. "Incompatible With Secondary Licenses" - means - - (a) that the initial Contributor has attached the notice described - in Exhibit B to the Covered Software; or - - (b) that the Covered Software was made available under the terms of - version 1.1 or earlier of the License, but not also under the - terms of a Secondary License. - -1.6. "Executable Form" - means any form of the work other than Source Code Form. - -1.7. "Larger Work" - means a work that combines Covered Software with other material, in - a separate file or files, that is not Covered Software. - -1.8. "License" - means this document. - -1.9. "Licensable" - means having the right to grant, to the maximum extent possible, - whether at the time of the initial grant or subsequently, any and - all of the rights conveyed by this License. - -1.10. "Modifications" - means any of the following: - - (a) any file in Source Code Form that results from an addition to, - deletion from, or modification of the contents of Covered - Software; or - - (b) any new file in Source Code Form that contains any Covered - Software. - -1.11. "Patent Claims" of a Contributor - means any patent claim(s), including without limitation, method, - process, and apparatus claims, in any patent Licensable by such - Contributor that would be infringed, but for the grant of the - License, by the making, using, selling, offering for sale, having - made, import, or transfer of either its Contributions or its - Contributor Version. - -1.12. "Secondary License" - means either the GNU General Public License, Version 2.0, the GNU - Lesser General Public License, Version 2.1, the GNU Affero General - Public License, Version 3.0, or any later versions of those - licenses. - -1.13. "Source Code Form" - means the form of the work preferred for making modifications. - -1.14. "You" (or "Your") - means an individual or a legal entity exercising rights under this - License. For legal entities, "You" includes any entity that - controls, is controlled by, or is under common control with You. For - purposes of this definition, "control" means (a) the power, direct - or indirect, to cause the direction or management of such entity, - whether by contract or otherwise, or (b) ownership of more than - fifty percent (50%) of the outstanding shares or beneficial - ownership of such entity. - -2. License Grants and Conditions --------------------------------- - -2.1. Grants - -Each Contributor hereby grants You a world-wide, royalty-free, -non-exclusive license: - -(a) under intellectual property rights (other than patent or trademark) - Licensable by such Contributor to use, reproduce, make available, - modify, display, perform, distribute, and otherwise exploit its - Contributions, either on an unmodified basis, with Modifications, or - as part of a Larger Work; and - -(b) under Patent Claims of such Contributor to make, use, sell, offer - for sale, have made, import, and otherwise transfer either its - Contributions or its Contributor Version. - -2.2. Effective Date - -The licenses granted in Section 2.1 with respect to any Contribution -become effective for each Contribution on the date the Contributor first -distributes such Contribution. - -2.3. Limitations on Grant Scope - -The licenses granted in this Section 2 are the only rights granted under -this License. No additional rights or licenses will be implied from the -distribution or licensing of Covered Software under this License. -Notwithstanding Section 2.1(b) above, no patent license is granted by a -Contributor: - -(a) for any code that a Contributor has removed from Covered Software; - or - -(b) for infringements caused by: (i) Your and any other third party's - modifications of Covered Software, or (ii) the combination of its - Contributions with other software (except as part of its Contributor - Version); or - -(c) under Patent Claims infringed by Covered Software in the absence of - its Contributions. - -This License does not grant any rights in the trademarks, service marks, -or logos of any Contributor (except as may be necessary to comply with -the notice requirements in Section 3.4). - -2.4. Subsequent Licenses - -No Contributor makes additional grants as a result of Your choice to -distribute the Covered Software under a subsequent version of this -License (see Section 10.2) or under the terms of a Secondary License (if -permitted under the terms of Section 3.3). - -2.5. Representation - -Each Contributor represents that the Contributor believes its -Contributions are its original creation(s) or it has sufficient rights -to grant the rights to its Contributions conveyed by this License. - -2.6. Fair Use - -This License is not intended to limit any rights You have under -applicable copyright doctrines of fair use, fair dealing, or other -equivalents. - -2.7. Conditions - -Sections 3.1, 3.2, 3.3, and 3.4 are conditions of the licenses granted -in Section 2.1. - -3. Responsibilities -------------------- - -3.1. Distribution of Source Form - -All distribution of Covered Software in Source Code Form, including any -Modifications that You create or to which You contribute, must be under -the terms of this License. You must inform recipients that the Source -Code Form of the Covered Software is governed by the terms of this -License, and how they can obtain a copy of this License. You may not -attempt to alter or restrict the recipients' rights in the Source Code -Form. - -3.2. Distribution of Executable Form - -If You distribute Covered Software in Executable Form then: - -(a) such Covered Software must also be made available in Source Code - Form, as described in Section 3.1, and You must inform recipients of - the Executable Form how they can obtain a copy of such Source Code - Form by reasonable means in a timely manner, at a charge no more - than the cost of distribution to the recipient; and - -(b) You may distribute such Executable Form under the terms of this - License, or sublicense it under different terms, provided that the - license for the Executable Form does not attempt to limit or alter - the recipients' rights in the Source Code Form under this License. - -3.3. Distribution of a Larger Work - -You may create and distribute a Larger Work under terms of Your choice, -provided that You also comply with the requirements of this License for -the Covered Software. If the Larger Work is a combination of Covered -Software with a work governed by one or more Secondary Licenses, and the -Covered Software is not Incompatible With Secondary Licenses, this -License permits You to additionally distribute such Covered Software -under the terms of such Secondary License(s), so that the recipient of -the Larger Work may, at their option, further distribute the Covered -Software under the terms of either this License or such Secondary -License(s). - -3.4. Notices - -You may not remove or alter the substance of any license notices -(including copyright notices, patent notices, disclaimers of warranty, -or limitations of liability) contained within the Source Code Form of -the Covered Software, except that You may alter any license notices to -the extent required to remedy known factual inaccuracies. - -3.5. Application of Additional Terms - -You may choose to offer, and to charge a fee for, warranty, support, -indemnity or liability obligations to one or more recipients of Covered -Software. However, You may do so only on Your own behalf, and not on -behalf of any Contributor. You must make it absolutely clear that any -such warranty, support, indemnity, or liability obligation is offered by -You alone, and You hereby agree to indemnify every Contributor for any -liability incurred by such Contributor as a result of warranty, support, -indemnity or liability terms You offer. You may include additional -disclaimers of warranty and limitations of liability specific to any -jurisdiction. - -4. Inability to Comply Due to Statute or Regulation ---------------------------------------------------- - -If it is impossible for You to comply with any of the terms of this -License with respect to some or all of the Covered Software due to -statute, judicial order, or regulation then You must: (a) comply with -the terms of this License to the maximum extent possible; and (b) -describe the limitations and the code they affect. Such description must -be placed in a text file included with all distributions of the Covered -Software under this License. Except to the extent prohibited by statute -or regulation, such description must be sufficiently detailed for a -recipient of ordinary skill to be able to understand it. - -5. Termination --------------- - -5.1. The rights granted under this License will terminate automatically -if You fail to comply with any of its terms. However, if You become -compliant, then the rights granted under this License from a particular -Contributor are reinstated (a) provisionally, unless and until such -Contributor explicitly and finally terminates Your grants, and (b) on an -ongoing basis, if such Contributor fails to notify You of the -non-compliance by some reasonable means prior to 60 days after You have -come back into compliance. Moreover, Your grants from a particular -Contributor are reinstated on an ongoing basis if such Contributor -notifies You of the non-compliance by some reasonable means, this is the -first time You have received notice of non-compliance with this License -from such Contributor, and You become compliant prior to 30 days after -Your receipt of the notice. - -5.2. If You initiate litigation against any entity by asserting a patent -infringement claim (excluding declaratory judgment actions, -counter-claims, and cross-claims) alleging that a Contributor Version -directly or indirectly infringes any patent, then the rights granted to -You by any and all Contributors for the Covered Software under Section -2.1 of this License shall terminate. - -5.3. In the event of termination under Sections 5.1 or 5.2 above, all -end user license agreements (excluding distributors and resellers) which -have been validly granted by You or Your distributors under this License -prior to termination shall survive termination. - -************************************************************************ -* * -* 6. Disclaimer of Warranty * -* ------------------------- * -* * -* Covered Software is provided under this License on an "as is" * -* basis, without warranty of any kind, either expressed, implied, or * -* statutory, including, without limitation, warranties that the * -* Covered Software is free of defects, merchantable, fit for a * -* particular purpose or non-infringing. The entire risk as to the * -* quality and performance of the Covered Software is with You. * -* Should any Covered Software prove defective in any respect, You * -* (not any Contributor) assume the cost of any necessary servicing, * -* repair, or correction. This disclaimer of warranty constitutes an * -* essential part of this License. No use of any Covered Software is * -* authorized under this License except under this disclaimer. * -* * -************************************************************************ - -************************************************************************ -* * -* 7. Limitation of Liability * -* -------------------------- * -* * -* Under no circumstances and under no legal theory, whether tort * -* (including negligence), contract, or otherwise, shall any * -* Contributor, or anyone who distributes Covered Software as * -* permitted above, be liable to You for any direct, indirect, * -* special, incidental, or consequential damages of any character * -* including, without limitation, damages for lost profits, loss of * -* goodwill, work stoppage, computer failure or malfunction, or any * -* and all other commercial damages or losses, even if such party * -* shall have been informed of the possibility of such damages. This * -* limitation of liability shall not apply to liability for death or * -* personal injury resulting from such party's negligence to the * -* extent applicable law prohibits such limitation. Some * -* jurisdictions do not allow the exclusion or limitation of * -* incidental or consequential damages, so this exclusion and * -* limitation may not apply to You. * -* * -************************************************************************ - -8. Litigation -------------- - -Any litigation relating to this License may be brought only in the -courts of a jurisdiction where the defendant maintains its principal -place of business and such litigation shall be governed by laws of that -jurisdiction, without reference to its conflict-of-law provisions. -Nothing in this Section shall prevent a party's ability to bring -cross-claims or counter-claims. - -9. Miscellaneous ----------------- - -This License represents the complete agreement concerning the subject -matter hereof. If any provision of this License is held to be -unenforceable, such provision shall be reformed only to the extent -necessary to make it enforceable. Any law or regulation which provides -that the language of a contract shall be construed against the drafter -shall not be used to construe this License against a Contributor. - -10. Versions of the License ---------------------------- - -10.1. New Versions - -Mozilla Foundation is the license steward. Except as provided in Section -10.3, no one other than the license steward has the right to modify or -publish new versions of this License. Each version will be given a -distinguishing version number. - -10.2. Effect of New Versions - -You may distribute the Covered Software under the terms of the version -of the License under which You originally received the Covered Software, -or under the terms of any subsequent version published by the license -steward. - -10.3. Modified Versions - -If you create software not governed by this License, and you want to -create a new license for such software, you may create and use a -modified version of this License if you rename the license and remove -any references to the name of the license steward (except to note that -such modified license differs from this License). - -10.4. Distributing Source Code Form that is Incompatible With Secondary -Licenses - -If You choose to distribute Source Code Form that is Incompatible With -Secondary Licenses under the terms of this version of the License, the -notice described in Exhibit B of this License must be attached. - -Exhibit A - Source Code Form License Notice -------------------------------------------- - - This Source Code Form is subject to the terms of the Mozilla Public - License, v. 2.0. If a copy of the MPL was not distributed with this - file, You can obtain one at http://mozilla.org/MPL/2.0/. - -If it is not possible or desirable to put the notice in a particular -file, then You may include the notice in a location (such as a LICENSE -file in a relevant directory) where a recipient would be likely to look -for such a notice. - -You may add additional accurate notices of copyright ownership. - -Exhibit B - "Incompatible With Secondary Licenses" Notice ---------------------------------------------------------- - - This Source Code Form is "Incompatible With Secondary Licenses", as - defined by the Mozilla Public License, v. 2.0. diff --git a/resources/robots/a1/meshes/calf.dae b/resources/robots/a1/meshes/calf.dae deleted file mode 100644 index ac4b964b..00000000 --- a/resources/robots/a1/meshes/calf.dae +++ /dev/null @@ -1,162 +0,0 @@ - - - - - Blender User - Blender 2.80.75 commit date:2019-07-29, commit time:14:47, hash:f6cb5f54494e - - 2020-03-06T17:58:05 - 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Redistributions of source code must retain the above copyright notice, this - list of conditions and the following disclaimer. - -2. Redistributions in binary form must reproduce the above copyright notice, this - list of conditions and the following disclaimer in the documentation and/or - other materials provided with the distribution. - -3. The name of ANYbotics and ANYmal may not be used to endorse or promote products - derived from this software without specific prior written permission. - -THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND -ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED -WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. -IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, -INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT -NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR -PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, -WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) -ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE -POSSIBILITY OF SUCH DAMAGE. diff --git a/resources/robots/anymal_b/meshes/anymal_base.dae 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43 19 43 42 43 43 43 35 43 37 43 41 43 11 43 13 43 43 43 27 50 29 50 42 50 43 51 42 51 44 51 45 52 44 52 46 52 47 53 46 53 48 53 48 54 49 54 47 54 46 52 47 52 45 52 44 55 45 55 43 55 41 56 40 56 50 56 51 57 50 57 52 57 53 58 52 58 54 58 54 59 55 59 53 59 52 60 53 60 51 60 50 61 51 61 41 61 56 62 57 62 22 62 22 62 20 62 56 62 57 63 24 63 22 63 57 64 28 64 26 64 26 62 24 62 57 62 57 65 30 65 28 65 57 66 32 66 30 66 58 67 34 67 32 67 56 68 16 68 14 68 32 62 57 62 58 62 58 69 38 69 36 69 56 70 18 70 16 70 36 62 34 62 58 62 58 62 59 62 2 62 20 62 18 62 56 62 2 62 39 62 58 62 59 71 6 71 4 71 39 62 38 62 58 62 59 72 8 72 6 72 59 62 56 62 12 62 4 62 2 62 59 62 12 73 10 73 59 73 10 62 8 62 59 62 14 62 12 62 56 62 58 74 57 74 60 74 61 75 60 75 62 75 63 76 62 76 64 76 65 77 64 77 66 77 67 78 66 78 68 78 68 79 69 79 67 79 66 80 67 80 65 80 64 76 65 76 63 76 62 81 63 81 61 81 60 82 61 82 58 82 67 83 69 83 70 83 71 84 70 84 72 84 73 85 72 85 74 85 75 86 74 86 76 86 65 87 67 87 71 87 77 88 71 88 73 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1032 1469 1005 1469 1005 1469 1004 1469 1036 1469 998 1699 1005 1699 1032 1699 1003 1700 998 1700 1028 1700 1028 1700 1027 1700 1003 1700 1032 1699 1028 1699 998 1699 1000 1701 999 1701 1030 1701 1010 1645 1000 1645 1035 1645 1011 1646 1010 1646 1025 1646 1023 1702 1011 1702 1042 1702 1022 1648 1023 1648 1047 1648 1021 1649 1022 1649 1044 1649 1019 1703 1021 1703 1043 1703 1009 1651 1019 1651 1045 1651 1006 1652 1009 1652 1024 1652 1004 1704 1006 1704 1038 1704 1038 1705 1036 1705 1004 1705 1024 1655 1038 1655 1006 1655 1045 1656 1024 1656 1009 1656 1043 1706 1045 1706 1019 1706 1044 1649 1043 1649 1021 1649 1047 1658 1044 1658 1022 1658 1042 1707 1047 1707 1023 1707 1025 1659 1042 1659 1011 1659 1035 1660 1025 1660 1010 1660 1030 1708 1035 1708 1000 1708 1018 1709 1008 1709 1033 1709 1016 1627 1018 1627 1037 1627 1017 1710 1016 1710 1046 1710 1020 1711 1017 1711 1049 1711 1015 1630 1020 1630 1048 1630 1013 1631 1015 1631 1051 1631 1014 1712 1013 1712 1050 1712 1012 1713 1014 1713 1040 1713 1007 1634 1012 1634 1039 1634 1002 1714 1007 1714 1041 1714 1041 1715 1034 1715 1002 1715 1039 1637 1041 1637 1007 1637 1040 1713 1039 1713 1012 1713 1050 1712 1040 1712 1014 1712 1051 1639 1050 1639 1013 1639 1048 1630 1051 1630 1015 1630 1049 1716 1048 1716 1020 1716 1046 1710 1049 1710 1017 1710 1037 1642 1046 1642 1016 1642 1033 1717 1037 1717 1018 1717 996 1382 1002 1382 1034 1382 1034 1382 1029 1382 996 1382 999 1382 1001 1382 1031 1382 1031 1382 1030 1382 999 1382 997 1718 996 1718 1029 1718 1001 1719 997 1719 1026 1719 1026 1719 1031 1719 1001 1719 1029 1718 1026 1718 997 1718

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219 161 219 132 219 166 220 162 220 163 220 167 221 163 221 164 221 168 222 164 222 165 222 169 223 165 223 130 223 142 224 166 224 167 224 168 225 141 225 140 225 140 226 167 226 168 226 168 227 169 227 138 227 169 228 139 228 138 228 139 229 169 229 128 229 128 230 59 230 139 230 138 231 141 231 168 231 140 232 143 232 167 232 167 233 143 233 142 233 130 234 128 234 169 234 165 235 169 235 168 235 164 236 168 236 167 236 163 237 167 237 166 237 132 238 130 238 165 238 161 239 165 239 164 239 160 240 164 240 163 240 159 241 163 241 162 241 134 242 132 242 161 242 157 243 161 243 160 243 155 244 160 244 159 244 153 245 159 245 158 245 136 246 134 246 157 246 156 247 157 247 155 247 154 248 155 248 153 248 152 249 153 249 150 249 142 250 170 250 171 250 166 251 171 251 172 251 162 252 172 252 173 252 158 253 173 253 174 253 150 254 174 254 175 254 175 255 151 255 150 255 174 256 150 256 158 256 173 257 158 257 162 257 172 258 162 258 166 258 171 259 166 259 142 259 116 260 176 260 177 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1058 1303 1058 1347 1058 1330 1059 1208 1059 1207 1059 1218 1060 1339 1060 1335 1060 1342 1061 1344 1061 1226 1061 1347 1062 1348 1062 1349 1062 1349 1063 1350 1063 1347 1063 1294 1064 1374 1064 1369 1064 1303 1065 1348 1065 1347 1065 1347 1066 1353 1066 1264 1066 1351 1067 1266 1067 1270 1067 1356 1068 1250 1068 1249 1068 1345 1069 1243 1069 1241 1069 1360 1070 1242 1070 1243 1070 1357 1071 1249 1071 1245 1071 1358 1072 1245 1072 1244 1072 1316 1073 1161 1073 1164 1073 1359 1074 1244 1074 1242 1074 1350 1075 1353 1075 1347 1075 1364 1076 1298 1076 1297 1076 1362 1077 1301 1077 1299 1077 1365 1078 1297 1078 1296 1078 1375 1079 1285 1079 1284 1079 1367 1080 1277 1080 1274 1080 1354 1081 1263 1081 1257 1081 1366 1082 1296 1082 1277 1082 1264 1083 1352 1083 1347 1083 1369 1084 1370 1084 1294 1084 1369 1085 1371 1085 1372 1085 1369 1086 1373 1086 1371 1086 1372 1087 1370 1087 1369 1087 1302 1088 1373 1088 1369 1088 1311 387 1341 387 1166 387 1346 195 1375 195 1251 195 1234 1089 572 1089 573 1089 1237 1090 573 1090 567 1090 16 1091 1138 1091 15 1091 17 1092 1154 1092 13 1092 1148 1093 7 1093 3 1093 1232 1094 585 1094 581 1094 584 1095 1220 1095 580 1095 1220 1096 579 1096 580 1096 1221 1097 575 1097 576 1097 577 1098 1221 1098 576 1098 10 1099 1153 1099 9 1099 579 1100 1224 1100 578 1100 1143 1101 0 1101 1 1101 1138 1102 14 1102 15 1102 1147 1103 3 1103 4 1103 8 1104 1147 1104 4 1104 586 1105 1230 1105 583 1105 1137 1106 5 1106 6 1106 1344 1107 574 1107 570 1107 14 1108 1141 1108 1140 1108 1140 1109 1308 1109 14 1109 1342 1110 574 1110 1343 1110 1380 1111 1239 1111 569 1111 1306 1112 1305 1112 1376 1112 951 1113 1187 1113 950 1113 1203 1114 947 1114 952 1114 1212 1115 945 1115 946 1115 960 1116 1192 1116 959 1116 203 1117 1174 1117 207 1117 1216 1118 952 1118 948 1118 206 1119 1184 1119 202 1119 766 1120 1289 1120 765 1120 1153 1121 8 1121 9 1121 1283 1122 763 1122 759 1122 12 1123 1151 1123 11 1123 1292 1124 760 1124 764 1124 953 1125 1214 1125 949 1125 768 1126 1287 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954 1243 1337 1243 1363 1362 1362 1362 1299 1362 1323 1365 1322 1365 1157 1365 1321 1366 1320 1366 1175 1366 1319 1367 1317 1367 1183 1367 1320 199 1319 199 1176 199 397 1369 1379 1369 1303 1369 1316 1474 1164 1474 1304 1474 1355 385 1354 385 1257 385 1210 1883 1211 1883 1188 1883 1368 1884 1367 1884 1274 1884 1200 1885 1197 1885 1340 1885 1302 1886 1369 1886 1368 1886 1341 1887 1340 1887 1197 1887 1309 1888 1325 1888 1158 1888 1322 1889 1321 1889 1181 1889 1325 1890 1324 1890 1159 1890 1324 1891 1323 1891 1156 1891 1327 1892 1326 1892 1211 1892 1332 1893 1331 1893 1206 1893 1331 1894 1330 1894 1207 1894 1329 1895 1328 1895 1209 1895 1333 1050 1332 1050 1205 1050 1334 1896 1333 1896 1204 1896 1328 1897 1327 1897 1210 1897 1185 1898 1183 1898 1317 1898 1351 1899 1270 1899 1303 1899 1330 1900 1329 1900 1208 1900 1294 1901 1285 1901 1374 1901 1351 1902 1346 1902 1266 1902 1356 1903 1355 1903 1250 1903 1345 1904 1360 1904 1243 1904 1360 1905 1359 1905 1242 1905 1357 1906 1356 1906 1249 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1151 186 1149 186 1150 186 1176 2052 1182 2052 1174 2052 1176 2053 1174 2053 1180 2053 1150 186 1149 186 1153 186 1153 186 1149 186 1146 186 1176 2054 1180 2054 1175 2054 1180 186 1178 186 1175 186 1149 186 1160 186 1172 186 1147 2055 1146 2055 1173 2055 1175 2056 1178 2056 1181 2056 1148 2057 1147 2057 1171 2057 1156 2058 1179 2058 1177 2058 1144 186 1148 186 1223 186 1160 2059 1177 2059 1172 2059 1145 2060 1292 2060 1143 2060 1149 2061 1172 2061 1173 2061 1147 2062 1173 2062 1171 2062 1148 2063 1171 2063 1166 2063 1292 2064 1289 2064 1143 2064 1134 2065 1140 2065 1141 2065 1287 2066 1298 2066 1299 2066 1298 186 1287 186 1288 186 1296 2067 1297 2067 1288 2067 1274 2068 1277 2068 1278 2068 1302 2069 1276 2069 1282 2069 1282 2070 1276 2070 1275 2070 1277 2071 1296 2071 1280 2071 1298 2072 1288 2072 1297 2072 1134 2073 1141 2073 1138 2073 1134 2074 1138 2074 1139 2074 1301 2075 1287 2075 1299 2075 1275 2076 1276 2076 1273 2076 1273 186 1276 186 1274 186 1300 186 1287 186 1301 186 1134 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2125 1217 2126 1225 2126 1221 2126 1217 2127 1221 2127 1222 2127 1233 2128 1230 2128 1235 2128 1233 186 1258 186 1230 186 1199 2129 1208 2129 1188 2129 1217 2130 1222 2130 1224 2130 1233 186 1241 186 1261 186 1231 186 1230 186 1258 186 1217 2131 1224 2131 1219 2131 1208 186 1209 186 1188 186 1232 2132 1231 2132 1259 2132 1223 186 1232 186 1144 186 1209 2133 1210 2133 1188 2133 1187 186 1188 186 1211 186

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9 62 216 10 62 217 15 62 218 24 63 219 25 63 220 13 63 221 30 64 222 31 64 223 26 64 224 32 62 225 26 62 226 31 62 227 12 62 228 13 62 229 26 62 230 6 62 231 14 62 232 18 62 233 18 62 234 19 62 235 27 62 236 27 62 237 28 62 238 18 62 239 0 65 240 1 65 241 6 65 242 2 62 243 15 62 244 4 62 245 7 62 246 8 62 247 9 62 248 20 62 249 33 62 250 22 62 251 21 62 252 22 62 253 33 62 254 16 62 255 9 62 256 22 62 257 10 62 258 23 62 259 34 62 260 34 62 261 35 62 262 29 62 263 29 62 264 17 62 265 34 62 266 11 62 267 3 62 268 10 62 269 4 62 270 5 62 271 2 62 272 25 62 273 30 62 274 13 62 275 26 62 276 13 62 277 30 62 278 6 62 279 18 62 280 28 62 281 28 62 282 0 62 283 6 62 284 15 62 285 10 62 286 4 62 287 20 62 288 22 62 289 9 62 290 9 66 291 15 66 292 7 66 293 34 62 294 17 62 295 10 62 296 3 62 297 4 62 298 10 62 299 24 62 300 13 62 301 5 62 302 6 67 303 1 67 304 2 67 305 8 62 306 20 62 307 9 62 308 10 68 309 17 68 310 11 68 311 5 62 312 6 62 313 2 62 314 6 62 315 5 62 316 13 62 317

- - - - - -

36 0 0 7 0 1 15 0 2 15 1 3 37 1 4 36 1 5 38 2 6 8 2 7 7 2 8 7 3 9 36 3 10 38 3 11 39 4 12 9 4 13 16 4 14 40 5 15 16 5 16 22 5 17 41 6 18 22 6 19 21 6 20 42 7 21 21 7 22 33 7 23 43 8 24 33 8 25 20 8 26 44 9 27 20 9 28 8 9 29 8 9 30 38 9 31 44 9 32 20 10 33 44 10 34 43 10 35 33 7 36 43 7 37 42 7 38 21 11 39 42 11 40 41 11 41 22 12 42 41 12 43 40 12 44 16 13 45 40 13 46 39 13 47 45 14 48 10 14 49 9 14 50 9 14 51 39 14 52 45 14 53 46 15 54 11 15 55 17 15 56 47 16 57 17 16 58 29 16 59 48 17 60 29 17 61 35 17 62 49 18 63 35 18 64 34 18 65 50 19 66 34 19 67 23 19 68 51 20 69 23 20 70 10 20 71 10 21 72 45 21 73 51 21 74 23 22 75 51 22 76 50 22 77 34 23 78 50 23 79 49 23 80 35 17 81 49 17 82 48 17 83 29 24 84 48 24 85 47 24 86 17 15 87 47 15 88 46 15 89 52 25 90 3 25 91 11 25 92 11 26 93 46 26 94 52 26 95 53 27 96 4 27 97 3 27 98 3 28 99 52 28 100 53 28 101 54 29 102 5 29 103 4 29 104 4 29 105 53 29 106 54 29 107 55 30 108 24 30 109 5 30 110 5 31 111 54 31 112 55 31 113 56 32 114 25 32 115 24 32 116 24 33 117 55 33 118 56 33 119 57 34 120 13 34 121 12 34 122 58 35 123 12 35 124 26 35 125 59 36 126 26 36 127 32 36 128 60 37 129 32 37 130 31 37 131 61 38 132 31 38 133 30 38 134 62 39 135 30 39 136 25 39 137 25 40 138 56 40 139 62 40 140 30 41 141 62 41 142 61 41 143 31 37 144 61 37 145 60 37 146 32 42 147 60 42 148 59 42 149 26 43 150 59 43 151 58 43 152 12 44 153 58 44 154 57 44 155 63 45 156 6 45 157 13 45 158 13 45 159 57 45 160 63 45 161 64 46 162 0 46 163 28 46 164 65 47 165 28 47 166 27 47 167 66 48 168 27 48 169 19 48 170 67 49 171 19 49 172 18 49 173 68 50 174 18 50 175 14 50 176 69 51 177 14 51 178 6 51 179 6 52 180 63 52 181 69 52 182 14 53 183 69 53 184 68 53 185 18 54 186 68 54 187 67 54 188 19 48 189 67 48 190 66 48 191 27 55 192 66 55 193 65 55 194 28 56 195 65 56 196 64 56 197 37 57 198 15 57 199 2 57 200 2 57 201 70 57 202 37 57 203 71 58 204 1 58 205 0 58 206 0 59 207 64 59 208 71 59 209 70 60 210 2 60 211 1 60 212 1 61 213 71 61 214 70 61 215

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220.2975 -37.17462 205.1 210.2009 -43.66328 205.1 225.5 -23.22608 205.1 225.2832 -26.25739 205.1 222.1187 -34.74173 205.1 223.5752 -32.07442 205.1 224.6372 -29.22699 205.1 138.8 -52.52608 202.5 134.6302 -52.22785 202.5 130.5452 -51.33922 202.5 126.6283 -49.8783 202.5 114.1513 -39.06686 202.5 112.1478 -35.39774 202.5 110.6869 -31.48085 202.5 109.7982 -27.39591 202.5 116.6565 -42.4135 202.5 119.6126 -45.36954 202.5 122.9592 -47.87481 202.5 109.5 -23.22608 202.5 109.5 25.17392 202.5 109.5 3.823917 201 109.5 -1.876083 201 109.5 25.17392 201 109.5 -23.22608 201 109.7982 29.34374 202.5 110.6869 33.42868 202.5 112.1478 37.34558 202.5 122.9592 49.82265 202.5 126.6283 51.82613 202.5 130.5452 53.28706 202.5 134.6302 54.17568 202.5 114.1513 41.01469 202.5 119.6126 47.31738 202.5 116.6565 44.36133 202.5 138.8 54.47392 202.5 109.7982 29.34374 201 110.6869 33.42868 201 112.1478 37.34558 201 114.1513 41.01469 201 116.6565 44.36133 201 119.6126 47.31738 201 122.9592 49.82265 201 126.6283 51.82613 201 130.5452 53.28706 201 134.6302 54.17568 201 138.8 54.47392 201 204.2 54.47392 202.5 174.35 54.47392 201 168.65 54.47392 201 204.2 54.47392 201 208.3698 54.17568 202.5 212.4548 53.28706 202.5 216.3717 51.82613 202.5 228.8487 41.01469 202.5 230.8522 37.34558 202.5 232.3131 33.42868 202.5 233.2018 29.34374 202.5 220.0408 49.82265 202.5 226.3435 44.36133 202.5 223.3874 47.31738 202.5 233.5 25.17392 202.5 138.8 -52.52608 201 134.6302 -52.22785 201 130.5452 -51.33922 201 126.6283 -49.8783 201 122.9592 -47.87481 201 119.6126 -45.36954 201 116.6565 -42.4135 201 114.1513 -39.06686 201 112.1478 -35.39774 201 110.6869 -31.48085 201 109.7982 -27.39591 201 204.2 -52.52608 202.5 168.65 -52.52608 201 174.35 -52.52608 201 204.2 -52.52608 201 233.5 -23.22608 202.5 233.2018 -27.39591 202.5 232.3131 -31.48085 202.5 230.8522 -35.39774 202.5 220.0408 -47.87481 202.5 216.3717 -49.8783 202.5 212.4548 -51.33922 202.5 208.3698 -52.22785 202.5 223.3874 -45.36954 202.5 226.3435 -42.4135 202.5 228.8487 -39.06686 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3444 1407 3445 346 3446 346 3447 346 3448 346 3449 346 3444 346 3444 346 3445 346 3448 346 3447 346 3448 346 3445 346 3449 1408 3440 1408 3441 1408 3441 1408 3444 1408 3449 1408 3448 1410 3438 1410 3440 1410 3440 1410 3449 1410 3448 1410 3447 1412 3439 1412 3438 1412 3438 1412 3448 1412 3447 1412 3446 1413 3443 1413 3439 1413 3439 1413 3447 1413 3446 1413 3445 1415 3442 1415 3443 1415 3443 1415 3446 1415 3445 1415

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2265 3337 2150 3337 2152 3337 2153 3338 2154 3338 2286 3338 2155 3339 2156 3339 2285 3339 2157 3340 2159 3340 2284 3340 2158 3341 2161 3341 2283 3341 2160 3342 2163 3342 2282 3342 2162 3343 2165 3343 2281 3343 2281 3344 2165 3344 2164 3344 2166 3345 2167 3345 2279 3345 2168 3346 2169 3346 2278 3346 2170 3347 2171 3347 2277 3347 2172 3348 2132 3348 2276 3348 2131 3349 2134 3349 2275 3349 2133 3350 2136 3350 2274 3350 2135 3351 2138 3351 2273 3351 2273 3352 2138 3352 2137 3352 2139 3353 2140 3353 2271 3353 2141 3354 2142 3354 2270 3354 2143 3355 2144 3355 2269 3355 2145 3356 2147 3356 2268 3356 2146 3357 2149 3357 2267 3357 2148 3358 2151 3358 2266 3358 2266 3359 2151 3359 2150 3359 2242 3360 2265 3360 2287 3360 2264 3361 2287 3361 2286 3361 2263 3362 2286 3362 2285 3362 2262 3363 2285 3363 2284 3363 2261 3364 2284 3364 2283 3364 2260 3365 2283 3365 2282 3365 2259 3366 2282 3366 2281 3366 2258 3367 2281 3367 2280 3367 2257 3368 2280 3368 2279 3368 2256 3369 2279 3369 2278 3369 2255 3370 2278 3370 2277 3370 2254 3371 2277 3371 2276 3371 2253 3372 2276 3372 2275 3372 2252 3373 2275 3373 2274 3373 2251 3374 2274 3374 2273 3374 2250 3375 2273 3375 2272 3375 2249 3376 2272 3376 2271 3376 2248 3377 2271 3377 2270 3377 2247 3378 2270 3378 2269 3378 2246 3379 2269 3379 2268 3379 2245 3380 2268 3380 2267 3380 2244 3381 2267 3381 2266 3381 2243 3382 2266 3382 2265 3382 2219 3383 2242 3383 2264 3383 2241 3384 2264 3384 2263 3384 2240 1386 2263 1386 2262 1386 2239 3385 2262 3385 2261 3385 2238 3386 2261 3386 2260 3386 2237 3387 2260 3387 2259 3387 2236 3388 2259 3388 2258 3388 2235 3389 2258 3389 2257 3389 2234 3390 2257 3390 2256 3390 2233 3391 2256 3391 2255 3391 2232 3392 2255 3392 2254 3392 2231 3393 2254 3393 2253 3393 2230 3394 2253 3394 2252 3394 2229 3395 2252 3395 2251 3395 2228 3396 2251 3396 2250 3396 2227 3397 2250 3397 2249 3397 2226 3398 2249 3398 2248 3398 2225 3399 2248 3399 2247 3399 2224 3400 2247 3400 2246 3400 2223 3401 2246 3401 2245 3401 2222 3402 2245 3402 2244 3402 2221 1405 2244 1405 2243 1405 2220 3403 2243 3403 2242 3403 2173 3404 2219 3404 2241 3404 2218 3405 2241 3405 2240 3405 2216 3406 2240 3406 2239 3406 2214 3407 2239 3407 2238 3407 2212 3408 2238 3408 2237 3408 2210 3409 2237 3409 2236 3409 2208 3410 2236 3410 2235 3410 2206 3411 2235 3411 2234 3411 2204 3412 2234 3412 2233 3412 2202 3413 2233 3413 2232 3413 2200 3414 2232 3414 2231 3414 2198 3415 2231 3415 2230 3415 2196 3416 2230 3416 2229 3416 2194 3417 2229 3417 2228 3417 2192 3418 2228 3418 2227 3418 2190 3419 2227 3419 2226 3419 2188 3420 2226 3420 2225 3420 2186 3421 2225 3421 2224 3421 2184 3422 2224 3422 2223 3422 2182 3423 2223 3423 2222 3423 2180 3424 2222 3424 2221 3424 2178 3425 2221 3425 2220 3425 2176 3426 2220 3426 2219 3426 2174 3427 2173 3427 2218 3427 2217 3428 2218 3428 2216 3428 2215 3429 2216 3429 2214 3429 2213 3430 2214 3430 2212 3430 2211 3431 2212 3431 2210 3431 2209 3432 2210 3432 2208 3432 2207 3433 2208 3433 2206 3433 2205 3434 2206 3434 2204 3434 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2098 1639 2098 1602 2099 1604 2099 1638 2099 1600 2100 1603 2100 1637 2100 1628 2101 1601 2101 1636 2101 1598 2102 1599 2102 1635 2102 1596 2103 1597 2103 1634 2103 1593 2104 1595 2104 1633 2104 1591 2105 1594 2105 1632 2105 1629 1720 1592 1720 1631 1720 1650 1721 1630 1721 1631 1721 1651 1257 1631 1257 1632 1257 1652 2106 1632 2106 1633 2106 1653 1723 1633 1723 1634 1723 1654 1724 1634 1724 1635 1724 1655 1260 1635 1260 1636 1260 1656 1725 1636 1725 1637 1725 1657 2107 1637 2107 1638 2107 1658 1262 1638 1262 1639 1262 1659 1727 1639 1727 1640 1727 1660 2108 1640 2108 1641 2108 1661 1728 1641 1728 1642 1728 1662 2109 1642 2109 1643 2109 1663 1730 1643 1730 1644 1730 1664 1268 1644 1268 1645 1268 1665 1731 1645 1731 1646 1731 1666 1732 1646 1732 1647 1732 1667 2110 1647 2110 1648 2110 1668 1272 1648 1272 1649 1272 1669 2111 1649 2111 1630 2111 1630 2112 1650 2112 1669 2112 1649 1272 1669 1272 1668 1272 1648 1736 1668 1736 1667 1736 1647 1737 1667 1737 1666 1737 1646 2113 1666 2113 1665 2113 1645 2114 1665 2114 1664 2114 1644 1730 1664 1730 1663 1730 1643 1729 1663 1729 1662 1729 1642 1265 1662 1265 1661 1265 1641 2115 1661 2115 1660 2115 1640 1263 1660 1263 1659 1263 1639 2116 1659 2116 1658 2116 1638 1726 1658 1726 1657 1726 1637 1725 1657 1725 1656 1725 1636 1260 1656 1260 1655 1260 1635 1741 1655 1741 1654 1741 1634 1742 1654 1742 1653 1742 1633 1743 1653 1743 1652 1743 1632 1257 1652 1257 1651 1257 1631 1744 1651 1744 1650 1744 1670 2117 1661 2117 1662 2117 1662 0 1663 0 1670 0 1663 0 1664 0 1670 0 1664 0 1665 0 1670 0 1665 0 1666 0 1670 0 1666 0 1667 0 1670 0 1667 0 1668 0 1670 0 1668 2118 1669 2118 1670 2118 1669 0 1650 0 1670 0 1650 0 1651 0 1670 0 1651 0 1652 0 1670 0 1652 0 1653 0 1670 0 1653 2119 1654 2119 1670 2119 1654 0 1655 0 1670 0 1655 0 1656 0 1670 0 1656 0 1657 0 1670 0 1657 0 1658 0 1670 0 1658 0 1659 0 1670 0 1659 0 1660 0 1670 0 1660 0 1661 0 1670 0 1670 2120 1671 2120 1672 2120 1672 35 1673 35 1670 35 1673 35 1674 35 1670 35 1674 35 1675 35 1670 35 1675 35 1676 35 1670 35 1676 35 1677 35 1670 35 1677 2121 1678 2121 1670 2121 1678 35 1679 35 1670 35 1679 35 1680 35 1670 35 1680 35 1681 35 1670 35 1681 2122 1682 2122 1670 2122 1682 35 1683 35 1670 35 1683 35 1684 35 1670 35 1684 35 1685 35 1670 35 1685 35 1686 35 1670 35 1686 35 1687 35 1670 35 1687 35 1688 35 1670 35 1688 35 1689 35 1670 35 1689 35 1690 35 1670 35 1690 35 1671 35 1670 35

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186 2272 185 2272 231 2272 194 0 212 0 213 0 439 2292 242 2292 241 2292 186 0 232 0 214 0 242 2299 205 2299 206 2299 350 2310 351 2310 295 2310 221 2314 265 2314 264 2314 387 2315 388 2315 386 2315 272 2316 240 2316 415 2316 249 2317 248 2317 443 2317 284 2318 247 2318 423 2318 361 2321 474 2321 469 2321 366 2323 407 2323 333 2323 194 2325 193 2325 210 2325 428 2327 442 2327 321 2327 289 2328 323 2328 290 2328 443 2329 329 2329 429 2329 212 2330 210 2330 283 2330 301 2331 256 2331 434 2331 441 2332 284 2332 424 2332 260 2333 259 2333 220 2333 406 2334 333 2334 257 2334 202 2336 218 2336 219 2336 400 2337 253 2337 252 2337 393 2338 465 2338 466 2338 254 2340 255 2340 217 2340 244 2341 245 2341 208 2341 243 2342 207 2342 205 2342 237 2343 228 2343 223 2343 239 2344 240 2344 231 2344 255 2345 254 2345 433 2345 248 2346 249 2346 216 2346 250 2347 225 2347 216 2347 234 2348 222 2348 227 2348 256 2349 218 2349 217 2349 413 2350 238 2350 237 2350 432 2351 250 2351 249 2351 420 2352 243 2352 242 2352 247 2353 246 2353 422 2353 326 2354 435 2354 404 2354 182 0 181 0 222 0 440 2355 245 2355 244 2355 376 2356 449 2356 447 2356 409 2357 234 2357 233 2357 281 2359 314 2359 305 2359 389 2360 452 2360 459 2360 296 2361 295 2361 215 2361 385 2362 386 2362 421 2362 287 2363 340 2363 304 2363 397 2364 430 2364 395 2364 328 2365 329 2365 331 2365 358 2366 359 2366 400 2366 357 2367 356 2367 399 2367 197 0 215 0 216 0 349 2368 263 2368 324 2368 233 2369 268 2369 336 2369 355 2370 354 2370 341 2370 354 2371 355 2371 299 2371 347 2372 318 2372 269 2372 412 2373 237 2373 236 2373 444 2375 251 2375 250 2375 434 2376 256 2376 255 2376 398 2377 397 2377 396 2377 352 2378 335 2378 264 2378 188 0 211 0 206 0 348 2379 293 2379 320 2379 391 2381 424 2381 389 2381 470 2382 363 2382 469 2382 362 2383 401 2383 360 2383 180 0 221 0 227 0 274 2384 307 2384 345 2384 200 0 199 0 226 0 390 2385 459 2385 465 2385 280 2386 277 2386 278 2386 204 2387 220 2387 221 2387 433 2388 254 2388 253 2388 339 2389 441 2389 425 2389 341 2390 342 2390 344 2390 185 0 184 0 229 0 435 2391 342 2391 403 2391 211 2392 214 2392 273 2392 401 2393 433 2393 359 2393 372 2395 411 2395 370 2395 342 2396 434 2396 402 2396 437 2397 308 2397 417 2397 394 2398 427 2398 392 2398 365 2399 366 2399 364 2399 363 2400 364 2400 362 2400 449 2402 376 2402 374 2402 329 2403 442 2403 428 2403 422 2404 246 2404 245 2404 414 2405 239 2405 238 2405 253 2407 230 2407 226 2407 421 2408 244 2408 243 2408 196 0 224 0 215 0 308 2410 436 2410 377 2410 202 2412 201 2412 217 2412 379 2414 447 2414 446 2414 238 2415 229 2415 228 2415 415 2417 240 2417 239 2417 381 2418 446 2418 382 2418 275 2419 276 2419 274 2419 246 2420 209 2420 208 2420 351 2421 350 2421 328 2421 377 2422 436 2422 316 2422 369 2423 408 2423 367 2423 189 0 206 0 205 0 191 0 190 0 207 0 184 0 183 0 228 0 382 2424 384 2424 420 2424 309 2425 278 2425 345 2425 183 2426 182 2426 223 2426 472 2427 471 2427 466 2427 473 2428 472 2428 467 2428 469 2429 474 2429 473 2429 192 0 191 0 208 0 469 2430 468 2430 462 2430 468 2431 467 2431 461 2431 454 2432 453 2432 445 2432 450 2433 448 2433 447 2433 456 2438 457 2438 455 2438 458 2439 464 2439 457 2439 466 2440 465 2440 459 2440 461 2441 467 2441 466 2441 445 2442 453 2442 452 2442 456 2443 450 2443 449 2443 447 2444 448 2444 445 2444 411 2445 236 2445 235 2445 427 2446 321 2446 304 2446 358 2447 473 2447 474 2447 473 2448 358 2448 357 2448 472 2449 357 2449 396 2449 187 2450 214 2450 211 2450 193 2451 192 2451 209 2451 303 2452 331 2452 443 2452 382 2453 446 2453 445 2453 195 0 213 0 224 0 300 2454 299 2454 219 2454 375 2455 414 2455 373 2455 371 2456 458 2456 456 2456 368 2457 464 2457 458 2457 464 2458 368 2458 367 2458

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645 35 646 35 631 35 646 35 647 35 630 35 630 35 631 35 646 35 656 35 638 35 639 35 624 35 654 35 651 35 626 35 650 35 654 35 639 35 655 35 656 35 659 35 637 35 638 35 660 35 634 35 635 35 638 35 656 35 659 35 663 732 636 732 637 732 664 35 635 35 636 35 635 35 664 35 660 35 637 35 659 35 663 35 660 733 665 733 634 733 665 734 666 734 633 734 666 735 645 735 632 735 636 35 663 35 664 35 632 736 633 736 666 736 631 737 632 737 645 737 633 738 634 738 665 738 642 765 661 765 641 765 641 768 662 768 640 768 640 35 655 35 639 35 643 35 658 35 642 35 644 771 657 771 643 771 625 772 653 772 644 772 628 35 648 35 649 35 625 35 651 35 652 35 629 1830 647 1830 648 1830 627 35 649 35 650 35 624 2584 626 2584 654 2584 626 2585 627 2585 650 2585 642 35 658 35 661 35 641 35 661 35 662 35 640 2605 662 2605 655 2605 643 35 657 35 658 35 644 35 653 35 657 35 625 2606 652 2606 653 2606 628 35 629 35 648 35 625 2636 624 2636 651 2636 629 35 630 35 647 35 627 35 628 35 649 35

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56 72 56 97 56 98 0 90 0 70 0 67 57 68 57 92 57 65 58 66 58 93 58 73 59 74 59 101 59 70 60 71 60 98 60 66 61 67 61 99 61 95 62 102 62 63 62 72 63 73 63 96 63 63 64 64 64 95 64 102 65 103 65 62 65 103 66 104 66 61 66 104 0 105 0 60 0 105 67 106 67 89 67 107 68 108 68 84 68 108 69 109 69 83 69 109 70 110 70 82 70 110 71 111 71 81 71 111 72 112 72 80 72 112 73 113 73 79 73 114 74 115 74 77 74 115 75 116 75 76 75 69 76 70 76 90 76 61 77 62 77 103 77 89 0 60 0 105 0 106 78 117 78 88 78 117 79 118 79 87 79 118 80 119 80 86 80 119 0 107 0 85 0 83 0 84 0 108 0 81 81 82 81 110 81 79 82 80 82 112 82 113 83 114 83 78 83 76 84 77 84 115 84 62 85 63 85 102 85 88 86 89 86 106 86 86 0 87 0 118 0 84 87 85 87 107 87 80 88 81 88 111 88 77 89 78 89 114 89 60 90 61 90 104 90 85 91 86 91 119 91 78 92 79 92 113 92 116 93 100 93 75 93 87 94 88 94 117 94 75 95 76 95 116 95 82 0 83 0 109 0 74 0 75 0 100 0 81 97 52 97 51 97 114 99 124 99 125 99 115 100 125 100 126 100 88 101 37 101 36 101 73 102 44 102 43 102 87 103 38 103 37 103 95 104 138 104 139 104 84 105 39 105 48 105 102 106 139 106 140 106 78 107 54 107 59 107 108 108 148 108 149 108 101 109 128 109 129 109 98 110 131 110 132 110 94 111 137 111 138 111 104 112 141 112 142 112 92 113 134 113 135 113 99 114 135 114 136 114 103 115 140 115 141 115 90 116 132 116 133 116 105 117 142 117 143 117 117 118 144 118 145 118 118 119 145 119 146 119 106 120 143 120 144 120 119 121 146 121 147 121 60 122 32 122 31 122 65 123 29 123 28 123 67 124 33 124 58 124 96 125 129 125 130 125 175 127 146 127 145 127 164 128 135 128 134 128 152 129 123 129 122 129 167 130 136 130 166 130 162 131 131 131 161 131 168 132 137 132 167 132 170 135 141 135 140 135 173 136 142 136 172 136 139 137 168 137 169 137 155 138 124 138 154 138 176 139 147 139 146 139 160 140 129 140 159 140 161 141 130 141 160 141 174 142 143 142 173 142 121 143 150 143 151 143 163 144 134 144 133 144 172 145 141 145 171 145 158 146 129 146 128 146 179 147 148 147 178 147 150 148 149 148 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320 2073 315 2073 319 196 317 196 320 196 315 196 313 196 311 196 311 196 309 196 306 196 306 2074 322 2074 311 2074 317 196 315 196 320 196 225 2075 224 2075 110 2075 259 196 258 196 260 196 257 196 261 196 262 196 262 196 256 196 257 196 255 2076 254 2076 253 2076 253 2077 252 2077 255 2077 251 2078 250 2078 249 2078 249 196 248 196 251 196 247 196 246 196 243 196 245 2079 244 2079 243 2079 243 196 242 196 257 196 260 196 258 196 257 196 257 196 256 196 255 196 255 2080 252 2080 251 2080 251 2081 248 2081 247 2081 246 196 245 196 243 196 242 196 260 196 257 196 257 2082 255 2082 251 2082 251 2083 247 2083 257 2083 358 196 357 196 359 196 356 196 366 196 365 196 365 196 367 196 356 196 355 196 354 196 353 196 353 2084 352 2084 351 2084 351 196 350 196 348 196 349 196 348 196 350 196 347 196 364 196 348 196 363 196 362 196 364 196 361 2085 360 2085 356 2085 359 2086 357 2086 356 2086 356 196 367 196 351 196 355 196 353 196 351 196 351 196 348 196 356 196 364 196 362 196 361 196 360 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480 813

- - - - -

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6876 162 6877 12161 6879 12161 6878 12161 6879 12162 6881 12162 6880 12162 6881 162 6883 162 6882 162 6883 12163 6867 12163 6884 12163 6885 162 6884 162 6867 162 6869 12164 6873 12164 6871 12164 6873 12165 6867 12165 6876 12165 6877 12166 6876 12166 6879 12166 6881 162 6867 162 6883 162 6867 162 6873 162 6869 162 6876 12167 6881 12167 6879 12167 6787 162 6785 162 6788 162 6789 162 6791 162 6790 162 6791 162 6789 162 6792 162 6793 162 6792 162 6794 162 6796 12168 6799 12168 6798 12168 6799 12169 6797 12169 6800 12169 6801 12170 6805 12170 6802 12170 6803 12171 6805 12171 6804 12171 6805 162 6797 162 6806 162 6797 12172 6796 12172 6795 12172 6785 162 6787 162 6786 162 6788 162 6792 162 6789 162 6792 162 6785 162 6794 162 6796 162 6797 162 6799 162 6802 12173 6805 12173 6803 12173 6805 162 6801 162 6797 162 6785 162 6792 162 6788 162 6794 12174 6785 12174 6795 12174 6801 162 6800 162 6797 162 6060 12176 5957 12176 6059 12176 6881 12177 6143 12177 6880 12177 6791 12178 6394 12178 6391 12178 6086 12179 6095 12179 6085 12179 6620 12180 6338 12180 6619 12180 6841 12181 6319 12181 6840 12181 6018 12182 5974 12182 6017 12182 6062 12183 5941 12183 6061 12183 6063 162 6065 162 6064 162 6065 12184 6063 12184 6066 12184 6067 162 6069 162 6068 162 6069 12185 6071 12185 6070 12185 6071 12186 6055 12186 6072 12186 6073 12187 6055 12187 6054 12187 6055 12188 6057 12188 6056 12188 6057 12189 6059 12189 6058 12189 6059 12190 6061 12190 6060 12190 6061 162 6059 162 6062 162 6063 12191 6067 12191 6066 12191 6067 12192 6063 12192 6069 12192 6072 12193 6055 12193 6073 12193 6055 162 6059 162 6057 162 6059 12194 6063 12194 6062 12194 6063 12195 6071 12195 6069 12195 6071 162 6063 162 6055 162 6532 12204 6449 12204 6468 12204 6123 12210 6174 12210 6118 12210 6116 162 6114 162 6120 162 5639 12217 5849 12217 5641 12217 6256 162 6259 162 6136 162 6021 12223 5881 12223 5882 12223 6367 12235 6365 12235 6368 12235 6438 162 6436 162 6444 162 5620 12236 5618 12236 5615 12236 6515 12241 6523 12241 6538 12241 6832 12242 6304 12242 6831 12242 6089 12243 6100 12243 6099 12243 6846 12244 6322 12244 6845 12244 6667 12248 6258 12248 6273 12248 6820 12249 6459 12249 6819 12249 5999 12250 5894 12250 5998 12250 6583 12251 6367 12251 6582 12251 6010 12252 5891 12252 5890 12252 6857 12253 6214 12253 6208 12253 5996 12254 5921 12254 5995 12254 6088 12255 6097 12255 6087 12255 6524 7094 6486 7094 6487 7094 6573 12256 6385 12256 6572 12256 6673 12257 6283 12257 6266 12257 6576 12259 6378 12259 6575 12259 6530 12261 6485 12261 6529 12261 6839 12262 6319 12262 6320 12262 6711 12263 6240 12263 6237 12263 6664 12264 6281 12264 6271 12264 6831 12265 6310 12265 6830 12265 6825 12266 6466 12266 6465 12266 6817 12267 6464 12267 6440 12267 6769 162 6756 162 6755 162 6756 162 6769 162 6757 162 6758 162 6773 162 6759 162 6760 12268 6773 12268 6772 12268 6773 12269 6771 12269 6774 12269 6771 162 6773 162 6770 162 6761 12270 6763 12270 6762 12270 6763 12271 6765 12271 6764 12271 6765 12272 6767 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112.96 47.95663 181 112.96 47.95663 181 118.4876 47.87313 195 118.4876 47.87313 181 123.969 47.1546 195 123.969 47.1546 181 129.3314 45.81059 195 129.3314 45.81059 181 134.5037 43.8589 195 134.5037 43.8589 181 139.4174 41.32544 195 139.4174 41.32544 181 144.0071 38.24381 195 144.0071 38.24381 181 148.212 34.65488 195 148.212 34.65488 181 151.9764 30.60627 195 151.9764 30.60627 181 155.2503 26.15167 195 155.2503 26.15167 181 157.9903 21.35018 195 157.9903 21.35018 181 160.1601 16.26548 195 160.1601 16.26548 181 161.7308 10.96502 195 161.7308 10.96502 181 162.6816 5.519114 195 162.6816 5.519114 181 163 0 195 163 0 181 162.6816 -5.519114 195 162.6816 -5.519114 181 161.7308 -10.96502 195 161.7308 -10.96502 181 160.1601 -16.26548 195 160.1601 -16.26548 181 157.9903 -21.35018 195 157.9903 -21.35018 181 155.2503 -26.15167 195 155.2503 -26.15167 181 151.9764 -30.60627 195 151.9764 -30.60627 181 148.212 -34.65488 195 148.212 -34.65488 181 144.0071 -38.24381 195 144.0071 -38.24381 181 139.4174 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39.46152 181 98.18476 41.74023 181 93.37158 39.46152 181 109.3563 48.06352 181 95.67652 53.7839 181 95.09292 53.9194 181 105.8054 48.68765 181 96.1938 53.48163 181 102.3813 49.81602 181 94.49538 53.87596 181 93.93751 53.6575 181 99.15504 51.42517 181 87.36603 49.86345 195 94.49538 53.87596 195 95.09292 53.9194 195 105.8054 48.68765 195 109.3563 48.06352 195 95.67652 53.7839 195 93.93751 53.6575 195 87.36603 49.86345 195 102.3813 49.81602 195 99.15504 51.42517 195 96.1938 53.48163 192.5 100.6521 50.60597 192.4436 101.4259 50.23276 192.446 99.92362 50.98802 192.2573 99.15504 51.42517 192.2801 99.22265 51.38526 191.9708 103.0567 49.54939 191.6406 103.7052 49.31442 192.2798 102.1975 49.89252 191.189 104.3788 49.09142 190.645 104.9906 48.90705 190.1125 105.4402 48.78235 187.287 106.0935 48.61707 186.6094 105.8637 48.67307 188 106.1742 48.59797 188.7095 106.0943 48.61688 189.5131 105.8054 48.68765 189.3928 105.8627 48.67332 184.0281 103.0542 49.55033 184.3594 103.7052 49.31442 184.8117 104.3796 49.09116 185.355 104.9906 48.90705 185.8893 105.4415 48.782 183.5559 101.4223 50.23445 183.5 100.6521 50.60597 183.7749 102.3813 49.81602 183.7205 102.1983 49.89217 183.5542 99.92247 50.98865 191.9972 98.56341 51.78684 191.6406 98.01909 52.14001 191.1871 97.53244 52.47319 190.645 97.1189 52.76984 190.0469 96.79853 53.0086 189.3906 96.56472 53.18795 188.7054 96.42491 53.29731 188 96.37793 53.33442 183.7202 99.22171 51.38582 184.0031 98.56295 51.78714 184.3594 98.01909 52.14001 184.8101 97.53498 52.47139 185.355 97.1189 52.76984 185.9503 96.79977 53.00766 186.6094 96.56472 53.18795 187.2922 96.42523 53.29706 185.355 98.24849 45.01446 186.6094 98.56808 44.46092 183.5 96.4282 48.1673 183.7202 97.12349 46.96302 184.3594 97.75072 45.87663 190.645 98.24849 45.01446 191.6406 97.75072 45.87663 184.3594 95.10569 50.45796 183.7202 95.73292 49.37157 188 98.6782 44.27018 189.3906 98.56808 44.46092 192.2798 97.12349 46.96302 192.5 96.4282 48.1673 192.2798 95.73292 49.37157 185.355 94.60792 51.32012 191.6406 95.10569 50.45796 190.645 94.60792 51.32012 188 94.1782 52.06441 186.6094 94.28833 51.87367 189.3906 94.28833 51.87367 239.3594 95.10569 -50.45796 240.355 94.60792 -51.32012 245.645 98.24849 -45.01446 244.3906 98.56808 -44.46092 240.355 98.24849 -45.01446 239.3594 97.75072 -45.87663 241.6094 94.28833 -51.87367 243 94.1782 -52.06441 244.3906 94.28833 -51.87367 246.6406 95.10569 -50.45796 247.2798 95.73292 -49.37157 247.5 96.4282 -48.1673 247.2798 97.12349 -46.96302 246.6406 97.75072 -45.87663 238.7202 97.12349 -46.96302 238.5 96.4282 -48.1673 238.7202 95.73292 -49.37157 245.645 94.60792 -51.32012 243 98.6782 -44.27018 241.6094 98.56808 -44.46092 243 96.37793 -53.33442 243.7054 96.42491 -53.29731 244.3906 96.56472 -53.18795 245.0469 96.79853 -53.0086 245.645 97.1189 -52.76984 246.1871 97.53244 -52.47319 246.6406 98.01909 -52.14001 246.9972 98.56341 -51.78684 247.2798 99.22171 -51.38582 247.446 99.92362 -50.98802 247.5 100.6521 -50.60597 247.4436 101.4259 -50.23276 247.2251 102.3813 -49.81602 246.9708 103.0567 -49.54939 247.2795 102.1983 -49.89217 246.6406 103.7052 -49.31442 246.189 104.3788 -49.09142 245.645 104.9906 -48.90705 245.1125 105.4402 -48.78235 244.3906 105.8637 -48.67307 243.7095 106.0943 -48.61688 242.287 106.0935 -48.61707 241.6072 105.8627 -48.67332 243 106.1742 -48.59797 240.8893 105.4415 -48.782 240.355 104.9906 -48.90705 241.4869 105.8054 -48.68765 239.8117 104.3796 -49.09116 239.3594 103.7052 -49.31442 239.0281 103.0542 -49.55033 238.7202 102.1975 -49.89252 238.5559 101.4223 -50.23445 238.5 100.6521 -50.60597 238.5542 99.92247 -50.98865 238.7199 99.22265 -51.38526 239.0031 98.56295 -51.78714 239.3594 98.01909 -52.14001 238.7427 99.15504 -51.42517 239.8101 97.53498 -52.47139 240.355 97.1189 -52.76984 240.9503 96.79977 -53.00766 241.6094 96.56472 -53.18795 242.2922 96.42523 -53.29706 250 109.3563 -48.06352 250 112.96 -47.95663 236 112.96 -47.95663 236 102.3813 -49.81602 236 99.15504 -51.42517 236 105.8054 -48.68765 236 109.3563 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250 151.9764 -30.60627 236 148.212 -34.65488 250 148.212 -34.65488 236 144.0071 -38.24381 250 144.0071 -38.24381 236 139.4174 -41.32544 250 139.4174 -41.32544 236 134.5037 -43.8589 250 134.5037 -43.8589 236 129.3314 -45.81059 250 129.3314 -45.81059 236 123.969 -47.1546 250 123.969 -47.1546 236 118.4876 -47.87313 250 118.4876 -47.87313 236 95.67652 -53.7839 250 95.67652 -53.7839 236 95.09292 -53.9194 250 95.09292 -53.9194 236 94.49538 -53.87596 250 94.49538 -53.87596 236 93.93751 -53.6575 250 93.93751 -53.6575 236 87.36603 -49.86345 250 87.36603 -49.86345 236 93.37158 -39.46152 250 93.37158 -39.46152 250 98.18476 -41.74023 250 103.2334 -43.4344 236 98.18476 -41.74023 250 108.4468 -44.52029 236 103.2334 -43.4344 250 113.7521 -44.98269 236 108.4468 -44.52029 250 119.0747 -44.81513 236 113.7521 -44.98269 250 124.3404 -44.01996 236 119.0747 -44.81513 250 129.4752 -42.60832 236 124.3404 -44.01996 250 134.4073 -40.59996 236 129.4752 -42.60832 250 139.0676 -38.02302 236 134.4073 -40.59996 250 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61.03769 216.9315 131.6618 60.93241 216.825 132.3237 60.81569 216.9315 132.9856 60.69898 217.2404 133.5827 60.5937 217.7216 134.0566 60.51014 218.3279 134.3608 60.45649 219 134.4657 60.43801 219.6721 134.3608 60.45649 220.2784 134.0566 60.51014 220.7596 133.5827 60.5937 221.0685 132.9856 60.69898 215.175 132.3237 60.81569 215.0685 131.6618 60.93241 214.7596 131.0647 61.03769 214.2784 130.5908 61.12125 213.6721 130.2866 61.17489 213 130.1818 61.19338 212.3279 130.2866 61.17489 211.7216 130.5908 61.12125 211.2404 131.0647 61.03769 210.9315 131.6618 60.93241 210.825 132.3237 60.81569 210.9315 132.9856 60.69898 211.2404 133.5827 60.5937 211.7216 134.0566 60.51014 212.3279 134.3608 60.45649 213 134.4657 60.43801 213.6721 134.3608 60.45649 214.2784 134.0566 60.51014 214.7596 133.5827 60.5937 215.0685 132.9856 60.69898 208.675 144.1414 58.73191 208.5685 143.4795 58.84863 208.2596 142.8824 58.95391 207.7784 142.4085 59.03747 207.1721 142.1043 59.09112 206.5 141.9994 59.1096 205.8279 142.1043 59.09112 205.2216 142.4085 59.03747 204.7404 142.8824 58.95391 204.4315 143.4795 58.84863 204.325 144.1414 58.73191 204.4315 144.8033 58.6152 204.7404 145.4004 58.50992 205.2216 145.8743 58.42636 205.8279 146.1785 58.37271 206.5 146.2834 58.35423 207.1721 146.1785 58.37271 207.7784 145.8743 58.42636 208.2596 145.4004 58.50992 208.5685 144.8033 58.6152 204.025 144.1935 59.02736 204.1461 144.9467 58.89455 204.4977 145.6262 58.77474 205.0452 146.1654 58.67966 205.7352 146.5116 58.61861 206.5 146.6309 58.59758 207.2648 146.5116 58.61861 207.9548 146.1654 58.67966 208.5023 145.6262 58.77474 208.8539 144.9467 58.89455 208.975 144.1935 59.02736 208.8539 143.4403 59.16017 208.5023 142.7608 59.27997 207.9548 142.2216 59.37506 207.2648 141.8754 59.4361 206.5 141.7561 59.45714 205.7352 141.8754 59.4361 205.0452 142.2216 59.37506 204.4977 142.7608 59.27997 204.1461 143.4403 59.16017 208.5225 145.6406 58.77219 208.8776 144.9543 58.89321 209 144.1935 59.02736 208.8776 143.4327 59.16151 208.5225 142.7464 59.28253 207.9695 142.2017 59.37857 207.2725 141.852 59.44023 206.5 141.7315 59.46148 207.2725 146.535 58.61449 207.9695 146.1853 58.67615 204.4775 142.7464 59.28253 204.1224 143.4327 59.16151 204 144.1935 59.02736 205.0305 146.1853 58.67615 205.7275 146.535 58.61449 206.5 146.6555 58.59324 205.7275 141.852 59.44023 205.0305 142.2017 59.37857 204.1224 144.9543 58.89321 204.4775 145.6406 58.77219 209 144.2803 59.51976 208.8776 143.5195 59.65391 208.5225 142.8332 59.77493 207.9695 142.2885 59.87097 207.2725 141.9388 59.93264 206.5 141.8183 59.95388 205.7275 141.9388 59.93264 205.0305 142.2885 59.87097 204.4775 142.8332 59.77493 204.1224 143.5195 59.65391 204 144.2803 59.51976 204.1224 145.0411 59.38561 204.4775 145.7275 59.26459 205.0305 146.2721 59.16855 205.7275 146.6219 59.10689 206.5 146.7424 59.08564 207.2725 146.6219 59.10689 207.9695 146.2721 59.16855 208.5225 145.7275 59.26459 208.8776 145.0411 59.38561 210.525 132.3758 61.11114 210.6461 133.129 60.97833 210.9977 133.8085 60.85852 211.5452 134.3477 60.76344 212.2352 134.6939 60.70239 213 134.8132 60.68136 213.7648 134.6939 60.70239 214.4548 134.3477 60.76344 215.0023 133.8085 60.85852 215.3539 133.129 60.97833 215.475 132.3758 61.11114 215.3539 131.6226 61.24394 215.0023 130.9431 61.36375 214.4548 130.4039 61.45883 213.7648 130.0577 61.51988 213 129.9384 61.54092 212.2352 130.0577 61.51988 211.5452 130.4039 61.45883 210.9977 130.9431 61.36375 210.6461 131.6226 61.24394 212.2275 134.7173 60.69826 213 134.8378 60.67702 213.7725 134.7173 60.69826 214.4695 134.3676 60.75992 215.3776 133.1366 60.97698 215.5 132.3758 61.11114 215.3776 131.615 61.24529 211.5305 134.3676 60.75992 215.0225 133.8229 60.85597 215.0225 130.9287 61.36631 213.7725 130.0343 61.52401 213 129.9138 61.54526 212.2275 130.0343 61.52401 214.4695 130.384 61.46235 211.5305 130.384 61.46235 210.9775 130.9287 61.36631 210.5 132.3758 61.11114 210.6224 133.1366 60.97698 210.9775 133.8229 60.85597 210.6224 131.615 61.24529 215.5 132.4626 61.60354 215.3776 131.7018 61.73769 215.0225 131.0155 61.85871 214.4695 130.4708 61.95475 213.7725 130.1211 62.01641 213 130.0006 62.03766 212.2275 130.1211 62.01641 211.5305 130.4708 61.95475 210.9775 131.0155 61.85871 210.6224 131.7018 61.73769 210.5 132.4626 61.60354 210.6224 133.2234 61.46939 210.9775 133.9098 61.34837 211.5305 134.4544 61.25233 212.2275 134.8041 61.19067 213 134.9246 61.16942 213.7725 134.8041 61.19067 214.4695 134.4544 61.25233 215.0225 133.9098 61.34837 215.3776 133.2234 61.46939 216.525 132.3758 61.11114 216.6461 133.129 60.97833 216.9977 133.8085 60.85852 217.5452 134.3477 60.76344 218.2352 134.6939 60.70239 219 134.8132 60.68136 219.7648 134.6939 60.70239 220.4548 134.3477 60.76344 221.0023 133.8085 60.85852 221.3539 133.129 60.97833 221.475 132.3758 61.11114 221.3539 131.6226 61.24394 221.0023 130.9431 61.36375 220.4548 130.4039 61.45883 219.7648 130.0577 61.51988 219 129.9384 61.54092 218.2352 130.0577 61.51988 217.5452 130.4039 61.45883 216.9977 130.9431 61.36375 216.6461 131.6226 61.24394 216.5 132.3758 61.11114 216.6224 133.1366 60.97698 216.9775 133.8229 60.85597 217.5305 134.3676 60.75992 218.2275 134.7173 60.69826 219 134.8378 60.67702 219.7725 134.7173 60.69826 220.4695 134.3676 60.75992 221.0225 133.8229 60.85597 221.3776 133.1366 60.97698 221.5 132.3758 61.11114 221.3776 131.615 61.24529 220.4695 130.384 61.46235 219.7725 130.0343 61.52401 219 129.9138 61.54526 221.0225 130.9287 61.36631 218.2275 130.0343 61.52401 217.5305 130.384 61.46235 216.9775 130.9287 61.36631 216.6224 131.615 61.24529 221.5 132.4626 61.60354 221.3776 131.7018 61.73769 221.0225 131.0155 61.85871 220.4695 130.4708 61.95475 219.7725 130.1211 62.01641 219 130.0006 62.03766 218.2275 130.1211 62.01641 217.5305 130.4708 61.95475 216.9775 131.0155 61.85871 216.6224 131.7018 61.73769 216.5 132.4626 61.60354 216.6224 133.2234 61.46939 216.9775 133.9098 61.34837 217.5305 134.4544 61.25233 218.2275 134.8041 61.19067 219 134.9246 61.16942 219.7725 134.8041 61.19067 220.4695 134.4544 61.25233 221.0225 133.9098 61.34837 221.3776 133.2234 61.46939 210.525 119.5733 63.36856 210.6461 120.3265 63.23575 210.9977 121.006 63.11594 211.5452 121.5452 63.02086 212.2352 121.8914 62.95982 213 122.0107 62.93878 213.7648 121.8914 62.95982 214.4548 121.5452 63.02086 215.0023 121.006 63.11594 215.3539 120.3265 63.23575 215.475 119.5733 63.36856 215.3539 118.8201 63.50137 215.0023 118.1407 63.62118 214.4548 117.6014 63.71626 213.7648 117.2552 63.7773 213 117.1359 63.79834 212.2352 117.2552 63.7773 211.5452 117.6014 63.71626 210.9977 118.1407 63.62118 210.6461 118.8201 63.50137 212.2275 121.9148 62.95569 213 122.0353 62.93444 213.7725 121.9148 62.95569 213.7725 117.2318 63.78144 213 117.1113 63.80268 212.2275 117.2318 63.78144 214.4695 121.5651 63.01735 215.3776 120.3341 63.23441 215.5 119.5733 63.36856 215.3776 118.8125 63.50271 214.4695 117.5815 63.71977 211.5305 121.5651 63.01735 215.0225 121.0205 63.11339 215.0225 118.1262 63.62373 211.5305 117.5815 63.71977 210.9775 118.1262 63.62373 210.6224 118.8125 63.50271 210.5 119.5733 63.36856 210.6224 120.3341 63.23441 210.9775 121.0205 63.11339 215.5 119.6601 63.86096 215.3776 118.8993 63.99512 215.0225 118.213 64.11614 214.4695 117.6683 64.21218 213.7725 117.3186 64.27384 213 117.1981 64.29509 212.2275 117.3186 64.27384 211.5305 117.6683 64.21218 210.9775 118.213 64.11614 210.6224 118.8993 63.99512 210.5 119.6601 63.86096 210.6224 120.4209 63.72682 210.9775 121.1073 63.6058 211.5305 121.652 63.50976 212.2275 122.0017 63.44809 213 122.1222 63.42684 213.7725 122.0017 63.44809 214.4695 121.652 63.50976 215.0225 121.1073 63.6058 215.3776 120.4209 63.72682 216.525 119.5733 63.36856 216.6461 120.3265 63.23575 216.9977 121.006 63.11594 217.5452 121.5452 63.02086 218.2352 121.8914 62.95982 219 122.0107 62.93878 219.7648 121.8914 62.95982 220.4548 121.5452 63.02086 221.0023 121.006 63.11594 221.3539 120.3265 63.23575 221.475 119.5733 63.36856 221.3539 118.8201 63.50137 221.0023 118.1407 63.62118 220.4548 117.6014 63.71626 219.7648 117.2552 63.7773 219 117.1359 63.79834 218.2352 117.2552 63.7773 217.5452 117.6014 63.71626 216.9977 118.1407 63.62118 216.6461 118.8201 63.50137 221.3776 120.3341 63.23441 221.5 119.5733 63.36856 221.3776 118.8125 63.50271 220.4695 117.5815 63.71977 219.7725 117.2318 63.78144 219 117.1113 63.80268 221.0225 118.1262 63.62373 218.2275 117.2318 63.78144 216.5 119.5733 63.36856 216.6224 120.3341 63.23441 216.9775 121.0205 63.11339 217.5305 121.5651 63.01735 218.2275 121.9148 62.95569 219 122.0353 62.93444 219.7725 121.9148 62.95569 217.5305 117.5815 63.71977 220.4695 121.5651 63.01735 216.9775 118.1262 63.62373 216.6224 118.8125 63.50271 221.0225 121.0205 63.11339 221.5 119.6601 63.86096 221.3776 118.8993 63.99512 221.0225 118.213 64.11614 220.4695 117.6683 64.21218 219.7725 117.3186 64.27384 219 117.1981 64.29509 218.2275 117.3186 64.27384 217.5305 117.6683 64.21218 216.9775 118.213 64.11614 216.6224 118.8993 63.99512 216.5 119.6601 63.86096 216.6224 120.4209 63.72682 216.9775 121.1073 63.6058 217.5305 121.652 63.50976 218.2275 122.0017 63.44809 219 122.1222 63.42684 219.7725 122.0017 63.44809 220.4695 121.652 63.50976 221.0225 121.1073 63.6058 221.3776 120.4209 63.72682 204.025 107.7556 65.45234 204.1461 108.5088 65.31954 204.4977 109.1883 65.19972 205.0452 109.7275 65.10466 205.7352 110.0737 65.0436 206.5 110.193 65.02256 207.2648 110.0737 65.0436 207.9548 109.7275 65.10466 208.5023 109.1883 65.19972 208.8539 108.5088 65.31954 208.975 107.7556 65.45234 208.8539 107.0024 65.58515 208.5023 106.323 65.70497 207.9548 105.7837 65.80004 207.2648 105.4375 65.86109 206.5 105.3182 65.88212 205.7352 105.4375 65.86109 205.0452 105.7837 65.80004 204.4977 106.323 65.70497 204.1461 107.0024 65.58515 208.5225 109.2028 65.19718 208.8776 108.5164 65.31819 209 107.7556 65.45234 208.8776 106.9948 65.5865 208.5225 106.3085 65.70751 207.9695 105.7638 65.80355 207.2725 105.4141 65.86521 206.5 105.2936 65.88646 207.2725 110.0971 65.03947 207.9695 109.7474 65.10113 204.4775 106.3085 65.70751 204.1224 106.9948 65.5865 204 107.7556 65.45234 205.0305 109.7474 65.10113 205.7275 110.0971 65.03947 206.5 110.2176 65.01822 205.7275 105.4141 65.86521 205.0305 105.7638 65.80355 204.1224 108.5164 65.31819 204.4775 109.2028 65.19718 209 107.8424 65.94475 208.8776 107.0816 66.07891 208.5225 106.3953 66.19992 207.9695 105.8506 66.29596 207.2725 105.5009 66.35762 206.5 105.3804 66.37887 205.7275 105.5009 66.35762 205.0305 105.8506 66.29596 204.4775 106.3953 66.19992 204.1224 107.0816 66.07891 204 107.8424 65.94475 204.1224 108.6033 65.8106 204.4775 109.2896 65.68959 205.0305 109.8343 65.59354 205.7275 110.184 65.53189 206.5 110.3045 65.51063 207.2725 110.184 65.53189 207.9695 109.8343 65.59354 208.5225 109.2896 65.68959 208.8776 108.6033 65.8106 198.3 138.3715 60.56165 201.15 138.3715 60.56165 201.15 126.0614 62.73225 209.75 138.3715 60.56165 209.75 126.0614 62.73225 216 126.0614 62.73225 216 138.3715 60.56165 223.7 138.3715 60.56165 223.7 126.0614 62.73225 198.3 126.0614 62.73225 201.15 113.7513 64.90287 209.75 113.7513 64.90287 216 113.7513 64.90287 223.7 113.7513 64.90287 216 105.0357 66.43965 223.5981 106.6833 66.14913 223.3014 106.0757 66.25628 222.8363 105.5809 66.34352 222.2438 105.2429 66.40311 223.7 107.35 66.03157 221.5767 105.0917 66.42978 214.5286 104.7356 66.49257 207.4714 104.7356 66.49257 209.75 105.0357 66.43965 200.4233 105.0917 66.42978 201.5292 105.0122 66.4438 198.4019 106.6833 66.14913 199.7561 105.2429 66.40311 199.1637 105.5809 66.34352 198.3 107.35 66.03157 198.6986 106.0757 66.25628 199.7561 146.8798 59.0614 198.3 144.7727 59.43294 198.4019 145.4395 59.31538 198.6986 146.0471 59.20823 199.1637 146.5419 59.12099 200.4233 147.0311 59.03473 214.5286 147.3872 58.97194 207.4714 147.3872 58.97194 222.2438 146.8798 59.0614 223.3014 146.0471 59.20823 223.5981 145.4395 59.31538 222.8363 146.5419 59.12099 223.7 144.7727 59.43294 221.5767 147.0311 59.03473 198.3 113.7513 64.90287 221.6381 104.1672 65.78044 222.5374 104.3711 65.74449 223.3358 104.8267 65.66417 223.9628 105.4935 65.5466 224.3627 106.3125 65.40219 224.5 107.2111 65.24372 224.5 144.6338 58.64509 207.4508 103.8091 65.8436 200.3619 104.1672 65.78044 214.5491 103.8091 65.8436 224.5 104.2258 48.31322 224.4987 104.1281 48.26869 224.4228 103.4644 47.95333 224.3627 103.2138 47.82828 224.2877 102.9737 47.70539 224.0148 102.371 47.38354 223.9631 102.2828 47.33484 223.7886 102.0208 47.18774 223.374 101.5522 46.91542 223.0735 101.3033 46.76597 222.574 101.0075 46.58405 222.1784 100.8573 46.48983 221.6381 100.7543 46.42445 224.3627 145.5325 58.48664 223.9628 146.3515 58.34222 223.3358 147.0183 58.22464 222.5374 147.4738 58.14432 221.6381 147.6777 58.10837 197.5 107.2111 65.24372 197.6373 106.3125 65.40219 198.0372 105.4935 65.5466 198.6641 104.8267 65.66417 199.4626 104.3711 65.74449 216.8905 100.4467 46.31457 211.3864 100.3113 46.26548 214.5493 100.3601 46.28322 205.8805 100.4134 46.30255 200.3619 100.7543 46.42445 214.5491 148.0359 58.04522 207.4508 148.0359 58.04522 200.3619 147.6777 58.10837 222.2649 144.1705 38.87448 222.5401 144.0926 38.97457 221.8901 144.2401 38.78445 221.6381 144.2647 38.75239 223.2748 143.7484 39.40626 223.337 143.7079 39.45593 222.7819 144.0032 39.08828 223.6989 143.4226 39.79999 223.9635 143.1394 40.13111 224.2942 142.6028 40.73084 224.3627 142.4337 40.91273 224.027 143.0571 40.22529 224.4409 142.1681 41.19162 224.5 141.6485 41.71459 197.5 144.6338 58.64509 199.735 100.8845 46.50697 199.4599 100.992 46.57436 200.1099 100.7884 46.44615 198.7252 101.4631 46.8623 198.663 101.5181 46.89514 199.2181 101.1149 46.65064 198.3011 101.9039 47.12087 198.0365 102.2833 47.33513 197.7058 102.9926 47.71519 197.6373 103.2138 47.82826 197.973 102.3928 47.39551 197.5591 103.5587 47.99951 197.5 104.2258 48.31322 200.3341 100.5956 45.91891 205.867 100.2551 45.80082 205.8494 99.97143 45.35787 200.313 100.4384 45.64224 200.2974 100.3099 45.47332 205.8287 99.58209 45.00436 200.2808 100.1643 45.31909 200.2545 99.91732 45.11904 205.8066 99.11402 44.76478 200.2083 99.44539 44.881 211.3874 100.1531 45.7649 211.3887 99.87002 45.32273 211.3903 99.48167 44.96947 211.3919 99.01477 44.72943 216.906 100.2883 45.81247 216.9263 100.0045 45.36926 216.95 99.6148 45.01567 221.6596 100.638 46.01632 221.6659 100.5956 45.91891 221.687 100.4384 45.64224 221.7026 100.3099 45.47332 221.7192 100.1643 45.31909 216.9756 99.14636 44.77624 221.7546 99.82828 45.06147 221.7455 99.91732 45.11904 221.7917 99.44539 44.881 222.2474 100.7534 46.07558 222.3366 100.5651 45.69645 222.442 100.3012 45.37017 222.5585 99.97422 45.112 222.6807 99.59929 44.93404 222.6752 100.921 46.16091 222.8087 100.7491 45.7744 222.968 100.5002 45.44353 223.1451 100.1865 45.18459 223.3314 99.82363 45.01031 223.21 101.2495 46.3254 223.3974 101.1092 45.92434 223.6255 100.89 45.58441 223.8821 100.6037 45.32394 224.1532 100.2657 45.15696 223.5271 101.5242 46.46026 223.7451 101.4099 46.04693 224.0152 101.216 45.69934 224.3219 100.9538 45.43758 224.6475 100.6382 45.27681 223.9555 102.0369 46.70539 224.2121 101.97 46.26892 224.5415 101.8245 45.90679 224.9222 101.61 45.64261 225.3296 101.3405 45.4936 224.1822 102.4158 46.88113 224.4569 102.3828 46.42736 224.8196 102.2743 46.0543 225.2445 102.0978 45.78827 225.7017 101.866 45.64807 224.4427 103.0597 47.16907 224.7341 103.0818 46.68561 225.1384 103.038 46.29359 225.6235 102.932 46.02426 226.1507 102.7721 45.89912 224.5592 103.5756 47.39014 224.8535 103.6391 46.88267 225.28 103.6493 46.47509 225.8017 103.6055 46.20288 226.373 103.5114 46.08971 224.602 104.2607 47.67049 224.8883 104.3751 47.13098 225.3299 104.4602 46.70225 225.8843 104.5078 46.42567 226.4979 104.5133 46.32796 224.5979 104.3603 47.71 224.7424 104.4335 47.38178 224.882 104.4817 47.16583 225.0858 104.5336 46.93291 225.5457 104.6084 46.59772 225.3244 104.578 46.73398 226.5 104.6611 46.36117 225.882 104.6398 46.45671 197.4021 104.3603 47.71 197.118 104.4817 47.16583 197.427 103.6738 47.43125 196.6756 104.578 46.73398 197.1331 103.7449 46.9192 196.118 104.6398 46.45671 196.7034 103.7657 46.50862 195.5 104.6611 46.36117 196.1759 103.7343 46.23582 197.5513 103.0798 47.17782 197.2597 103.1035 46.69343 196.8543 103.0618 46.30081 196.3676 102.9581 46.03137 195.5973 103.6536 46.12492 197.8057 102.4393 46.89186 197.5301 102.4084 46.43702 197.1656 102.3021 46.06327 196.7382 102.1281 45.79713 195.8385 102.8006 45.9067 198.1359 101.9095 46.64527 197.8872 101.831 46.21458 197.5708 101.6733 45.85609 197.2066 101.4466 45.59252 196.2781 101.8987 45.65748 198.577 101.426 46.41236 198.369 101.3025 46.00342 198.1128 101.0995 45.65858 197.8229 100.8285 45.39728 196.8179 101.1651 45.44057 199.1012 101.0405 46.22116 198.9449 100.8802 45.82938 198.7572 100.642 45.49523 198.5475 100.3381 45.23572 197.5156 100.5048 45.23428 199.6588 100.7838 46.09111 199.5598 100.5984 45.71064 199.4427 100.3373 45.38353 199.3131 100.0126 45.12523 198.3265 99.98407 45.06409 200.0659 100.6762 46.036 200.0096 100.4803 45.66024 199.9433 100.2097 45.33606 199.8703 99.87659 45.07826 199.177 99.63987 44.94793 199.7937 99.49633 44.89862 197.5 123.5956 48.74798 197.5 115.8391 49.49288 197.5 119.732 49.2733 197.5 131.139 46.7951 197.5 134.7721 45.37965 197.5 127.4058 47.92021 197.5 108.0619 49.01135 197.5 111.941 49.40539 197.5 138.2825 43.68264 197.5 141.6485 41.71459 199.4626 147.4738 58.14432 198.6641 147.0183 58.22464 198.0372 146.3515 58.34222 197.6373 145.5325 58.48664 197.5013 141.7251 41.63931 197.5772 142.2409 41.11598 197.6373 142.4337 40.91276 197.7123 142.6172 40.71516 197.9852 143.0735 40.20658 198.0368 143.1397 40.13065 198.2114 143.3356 39.90284 198.626 143.6828 39.48665 198.9265 143.8656 39.26108 199.426 144.0813 38.98899 199.8215 144.1902 38.84908 200.3619 144.2647 38.75239 205.1095 144.5162 38.54393 210.6136 144.6266 38.4515 207.4506 144.5868 38.48485 216.1195 144.5433 38.52125 224.5979 141.3158 41.19376 224.8669 141.6237 40.24015 224.882 141.0156 40.72391 224.7424 141.1347 40.91036 224.573 141.8656 40.697 225.2966 141.4637 39.86144 225.3244 140.7774 40.35104 225.0858 140.8872 40.5228 225.8241 141.3999 39.59437 225.882 140.6245 40.11164 225.5457 140.7023 40.23339 226.5 140.5718 40.02915 224.4487 142.3371 40.25569 224.7403 142.1492 39.80862 225.1457 142.0541 39.42541 225.6324 142.0594 39.13675 226.4027 141.4378 39.46253 224.1943 142.8411 39.76793 224.4699 142.7146 39.32994 224.8343 142.6867 38.94239 225.2618 142.7592 38.63278 226.1614 142.1647 38.96575 223.8641 143.2547 39.35499 224.1127 143.1812 38.92342 224.4292 143.2067 38.53264 224.7933 143.3296 38.20741 225.7218 142.927 38.42309 223.423 143.6293 38.97077 223.631 143.6055 38.54426 223.8872 143.6783 38.15079 224.1771 143.8436 37.81256 225.1821 143.5422 37.96834 222.8988 143.9262 38.65926 223.055 143.9429 38.23625 223.2428 144.0524 37.8408 223.4525 144.2492 37.49302 224.4844 144.092 37.54868 222.3412 144.123 38.44923 222.4402 144.167 38.02832 222.5573 144.3005 37.63162 222.6869 144.5172 37.27787 223.6735 144.5232 37.21063 221.9341 144.2052 38.36066 221.9904 144.2608 37.94057 222.0567 144.4042 37.54337 222.1297 144.629 37.18719 222.823 144.8069 36.98376 221.6659 144.2409 38.22307 221.6596 144.2344 38.3291 221.687 144.294 37.9093 221.7026 144.3569 37.70665 221.7192 144.441 37.51192 221.7455 144.6047 37.23945 222.2063 144.9249 36.88833 221.7546 144.6687 37.15489 221.7917 144.9667 36.85435 216.1329 144.5205 37.99563 216.1506 144.6355 37.48238 216.1712 144.8805 37.01703 216.1934 145.2384 36.6318 210.6126 144.6041 37.92699 210.6113 144.7188 37.41468 210.6097 144.9629 36.94989 210.6081 145.3196 36.56464 205.094 144.4933 38.01795 205.0737 144.6084 37.50438 205.05 144.8536 37.03885 200.3404 144.2344 38.3291 200.3341 144.2409 38.22307 200.313 144.294 37.9093 200.2974 144.3569 37.70665 200.2808 144.441 37.51192 205.0244 145.2119 36.65364 200.2545 144.6047 37.23945 200.2083 144.9667 36.85435 199.7526 144.1462 38.42425 199.6634 144.1935 38.00358 199.558 144.3298 37.60673 199.4415 144.5488 37.25229 199.3248 144.0179 38.56177 199.1913 144.0473 38.13977 199.032 144.168 37.74372 198.8549 144.3741 37.39313 198.6686 144.6555 37.10523 199.3193 144.8403 36.95684 198.7899 143.7655 38.82868 198.6026 143.7601 38.40383 198.3745 143.8499 38.00942 198.1179 144.0298 37.66674 197.8468 144.2903 37.39424 198.4729 143.5535 39.04936 198.2549 143.5195 38.62188 197.9848 143.5828 38.22894 197.6781 143.7397 37.89326 197.3525 143.9812 37.63426 198.0445 143.1556 39.45505 197.7879 143.0691 39.02202 197.4585 143.0819 38.63199 197.0778 143.1931 38.31039 196.6704 143.3955 38.07817 197.8178 142.8595 39.74981 197.5431 142.7353 39.31212 197.1804 142.7098 38.92443 196.7555 142.7846 38.6141 196.2983 142.9545 38.40305 197.5572 142.353 40.24061 197.2659 142.1669 39.79384 196.8616 142.0739 39.4105 196.3765 142.0814 39.12115 195.8493 142.1889 38.94888 197.4408 141.9438 40.62478 197.1465 141.7106 40.16963 196.72 141.5615 39.79015 196.1983 141.5096 39.51936 195.6269 141.5594 39.38083 197.398 141.3959 41.12253 197.1117 141.1039 40.65469 196.6701 140.8773 40.28092 196.1157 140.738 40.03732 195.5021 140.6993 39.94739 197.4021 141.3158 41.19376 197.118 141.0156 40.72391 196.6756 140.7774 40.35104 196.118 140.6245 40.11164 195.5 140.5718 40.02915 197.4021 137.9918 43.13724 197.4021 134.5253 44.81306 197.4021 130.9375 46.21084 197.4021 127.2509 47.3219 197.4021 123.4883 48.13934 197.4021 119.673 48.65809 197.4021 115.8287 48.87494 197.4021 111.9792 48.78854 197.4021 108.1485 48.39942 197.118 137.7296 42.64522 197.118 134.3026 44.30194 197.118 130.7557 45.68377 197.118 127.1112 46.78215 197.118 123.3915 47.59027 197.118 119.6197 48.10311 197.118 115.8192 48.31748 197.118 112.0137 48.23207 197.118 108.2267 47.84739 196.6756 137.5215 42.25476 196.6756 134.1258 43.8963 196.6756 130.6115 45.26548 196.6756 127.0003 46.35381 196.6756 123.3146 47.15453 196.6756 119.5774 47.66267 196.6756 115.8117 47.87508 196.6756 112.041 47.79045 196.6756 108.2887 47.40929 196.118 137.3879 42.00407 196.118 134.0124 43.63587 196.118 130.5189 44.99693 196.118 126.9291 46.0788 196.118 123.2653 46.87477 196.118 119.5502 47.37989 196.118 115.8069 47.59104 196.118 112.0586 47.50691 196.118 108.3285 47.12802 195.5 137.3418 41.91768 195.5 133.9732 43.54613 195.5 130.4869 44.90439 195.5 126.9046 45.98403 195.5 123.2483 46.77837 195.5 119.5409 47.28245 195.5 115.8052 47.49317 195.5 112.0646 47.40921 195.5 108.3422 47.0311 224.5437 106.2114 48.29096 224.5437 110.0396 48.83288 224.7424 110.094 48.29715 224.5437 113.8986 49.07181 224.7424 113.9106 48.53346 224.5437 117.7643 49.00627 224.7424 117.734 48.46863 224.5437 121.613 48.63666 224.7424 121.5404 48.10308 224.5437 125.4206 47.96528 224.5437 129.1635 46.99629 224.7424 129.0081 46.4807 224.5437 132.8186 45.73571 224.7424 132.6231 45.23395 224.5437 136.3631 44.19135 224.7424 136.1287 43.70653 224.5437 139.775 42.3728 224.7424 139.5032 41.90793 224.7424 106.3079 47.76118 225.0858 110.1405 47.83961 225.0858 113.921 48.07368 225.0858 117.7081 48.00947 225.0858 121.4785 47.64738 224.7424 125.3062 47.43906 225.0858 125.2086 46.98965 225.0858 128.8754 46.04037 225.0858 132.4561 44.80543 225.0858 135.9285 43.29248 225.0858 139.2711 41.51092 225.0858 106.3902 47.30871 225.5457 110.1752 47.49795 225.5457 113.9287 47.73034 225.5457 117.6888 47.66659 225.5457 121.4322 47.30709 225.5457 125.1357 46.65406 225.5457 128.7763 45.71156 225.5457 132.3315 44.48543 225.5457 135.7791 42.98329 225.5457 139.0978 41.21446 225.5457 106.4517 46.97084 226.0842 110.1953 47.3003 226.0842 113.9331 47.53173 226.0842 117.6776 47.46825 226.0842 121.4054 47.11024 226.0842 125.0935 46.45993 226.0842 128.719 45.52135 226.0842 132.2593 44.30032 226.0842 135.6926 42.80443 226.0842 138.9975 41.04296 224.5 129.2835 47.39443 224.5 132.9695 46.12316 224.5 136.544 44.56572 224.5 139.9849 42.73176 226.5 138.9754 41.00523 226.5 135.6736 42.76509 226.5 132.2435 44.2596 226.5 128.7064 45.4795 226.5 125.0842 46.41722 226.5 121.3995 47.06693 226.5 117.6751 47.42461 226.5 113.9341 47.48804 226.0842 106.4873 46.77539 226.5 110.1997 47.25682 224.5 106.137 48.70007 224.5 109.9976 49.24658 224.5 113.8892 49.48754 224.5 117.7878 49.42144 224.5 121.4342 49.08005 224.5363 122.6632 48.51948 224.5511 123.4388 48.31921 224.5411 124.2279 48.22178 224.5 125.7402 48.32078 224.5005 125.577 48.29185 226.5 106.4951 46.73239 224.5 124.2279 48.22178 224.5 122.6632 48.51948 226.5 102.7004 43.28647 226.5 101.4575 45.52858 226.5 107.6073 44.38861 226.5 112.6682 44.93954 226.5 127.7695 43.15019 226.5 122.8142 44.31635 226.5 137.1286 39.1832 226.5 132.5614 41.43181 226.5 141.3627 36.46924 226.5 117.7588 44.91535 226.5 143.2975 38.15107 224.1909 97.41683 44.12575 224.1892 98.63827 41.92009 223.5579 98.31777 41.79358 224.7624 97.81591 44.28269 224.7624 99.03987 42.07462 225.2694 98.2915 44.46432 225.2726 99.52089 42.25396 225.7006 98.83543 44.66493 225.7006 100.0638 42.4489 226.0442 99.43393 44.87702 226.296 100.0808 45.09621 226.0464 100.6699 42.65734 226.296 101.3155 42.8688 226.3914 100.4462 45.21545 226.4442 101.9679 43.07163 223.5579 97.09653 43.99677 217.7404 95.89595 40.74353 217.3413 94.51821 42.85727 221.6142 96.28161 43.65629 222.4057 97.8321 41.59643 213.0579 94.00109 39.80007 213.0579 92.78881 41.98706 212.3939 92.5726 41.87196 212.3942 93.78482 39.68521 211.7046 93.65404 39.61502 210.2954 93.65404 39.61502 210.2954 92.44194 41.80171 210.9972 92.39804 41.77799 211.7046 92.44194 41.80171 211.0028 93.61013 39.59132 208.9421 92.78881 41.98706 209.6058 92.57266 41.87199 209.6061 93.78475 39.68517 208.9421 94.00109 39.80007 204.6587 94.51821 42.85727 204.2595 95.89595 40.74353 199.592 97.83308 41.59683 198.4421 97.09653 43.99677 198.4421 98.31777 41.79358 197.2376 97.81591 44.28269 197.8108 97.41584 44.12536 197.809 98.63929 41.92049 196.2994 98.83543 44.66493 196.7275 98.29489 44.46559 196.7305 99.51752 42.25272 197.2376 99.03987 42.07462 195.9536 99.43849 44.87861 196.2994 100.0638 42.4489 195.9558 100.6653 42.65579 195.7038 100.0815 45.09644 195.5 101.4575 45.52858 195.5556 101.9693 43.07204 195.5004 102.6411 43.2696 195.6086 100.4462 45.21545 195.704 101.3155 42.8688 195.5 102.7004 43.28647 195.5 141.3627 36.46924 195.5 143.2975 38.15107 195.5 137.1286 39.1832 195.5 132.5614 41.43181 195.5 107.6073 44.38861 195.5 127.7695 43.15019 195.5 122.8142 44.31635 195.5 112.6682 44.93954 195.5 117.7588 44.91535 224.7624 146.2933 35.73481 224.1892 146.6155 35.45013 224.1909 144.7117 33.79668 223.5579 146.8715 35.22009 223.5579 144.9704 33.56746 225.7006 145.466 36.4427 225.2725 145.9058 36.0705 225.2695 144.0001 34.40925 224.7624 144.388 34.07852 226.0464 144.9724 36.84975 225.7006 143.5538 34.78044 226.0442 143.0593 35.18059 226.2962 144.4427 37.27437 226.4444 141.9764 36.01771 226.4996 141.4126 36.43313 226.3914 144.1407 37.51094 226.296 142.5212 35.60312 217.3413 148.9046 33.26747 217.7404 146.8871 31.75242 222.408 145.3586 33.2168 213.0579 148.345 30.21778 213.0579 150.2321 31.85826 209.6061 150.3959 31.67614 209.6058 148.5089 30.03587 210.2954 148.6078 29.92519 208.9421 150.2321 31.85826 208.9421 148.345 30.21778 211.7046 148.6078 29.92519 211.7046 150.4947 31.56545 211.0028 150.5278 31.52814 210.2954 150.4947 31.56545 210.9972 148.6409 29.88791 212.3942 150.3959 31.6762 212.3939 148.509 30.03582 204.2595 146.8871 31.75242 204.6587 148.9046 33.26747 200.3858 147.5209 34.62142 198.4421 146.8715 35.22009 198.4421 144.9704 33.56746 199.5943 145.3594 33.21609 197.8091 146.6147 35.45085 197.8108 144.7125 33.79595 197.2376 146.2933 35.73481 197.2376 144.388 34.07852 196.7306 145.9085 36.06815 196.7274 143.9973 34.41156 196.2994 145.466 36.4427 196.2994 143.5538 34.78044 195.9558 144.9762 36.8467 195.704 144.4432 37.27392 195.9536 143.0555 35.18362 195.704 142.5212 35.60312 195.6086 144.1407 37.51094 195.5558 141.9775 36.01686 224.5 116.2193 57.73624 224.5 120.7987 56.92877 224.5 120.1489 53.24396 224.5 115.4778 53.76614 224.5 115.5696 54.05142 224.5 129.0218 55.47881 224.5 133.6929 54.95664 224.5 133.6012 54.67135 224.5 141.1746 49.53657 224.5 128.3721 51.794 224.5 141.8243 53.22138 224.5 120.1376 52.94449 224.5 128.2803 51.50871 224.5 141.0828 49.25128 224.5 116.2307 58.0357 224.5 116.1608 58.32712 224.5 134.0849 55.40261 224.5 128.8173 56.33143 224.5 128.6061 56.54406 224.5 129.0332 55.77828 224.5 133.8583 55.20657 224.5 140.9175 49.00135 224.5 115.3125 53.5162 224.5 120.5646 52.17871 224.5 121.388 51.08913 224.5 120.2075 52.65308 224.5 128.9633 56.06969 224.5 127.8883 51.06274 224.5 128.115 51.25878 224.5 141.8357 53.52085 224.5 139.2732 47.93549 224.5 140.4196 48.67773 224.5 107.9962 59.1862 224.5 107.3464 55.50139 224.5 120.3534 52.39134 224.5 116.0148 58.58886 224.5 115.8036 58.80149 224.5 113.6682 52.45034 224.5 138.1823 56.60946 224.5 136.817 56.6432 224.5 141.7658 53.81227 224.5 140.6908 48.80532 224.5 108.0879 59.47149 224.5 114.8146 53.19259 224.5 127.7827 57.63365 224.5 135.5025 56.27243 224.5 134.3561 55.53019 224.5 141.6198 54.074 224.5 141.4086 54.28664 224.5 108.2533 59.72143 224.5 114.9802 59.89107 224.5 113.8718 60.68905 224.5 115.0858 53.32017 224.5 139.4768 56.1742 224.5 108.48 59.91746 224.5 109.8976 60.78728 224.5 108.7511 60.04504 224.5 122.4964 50.29115 224.5 125.3798 58.86688 224.5 140.5852 55.37622 224.5 137.9588 47.56472 224.5 133.01 50.39566 224.5 133.1559 50.13392 224.5 112.5773 61.12431 224.5 112.3538 52.07957 224.5 107.5509 54.64877 224.5 107.7621 54.43614 224.5 107.3351 55.20192 224.5 107.405 54.91051 224.5 123.7909 49.85589 224.5 125.1563 49.82215 224.5 126.6744 58.43162 224.5 136.5934 47.59846 224.5 133.3671 49.92129 224.5 134.1905 48.8317 224.5 132.9401 50.68707 224.5 111.212 61.15806 224.5 109.6939 52.54858 224.5 110.9884 52.11332 224.5 108.5856 53.34655 224.5 126.4707 50.19292 224.5 127.6171 50.93516 224.5 135.2989 48.03372 224.5 132.9514 50.98654 224.5 121.2825 57.66003 224.5 121.0558 57.464 224.5 122.7001 58.52986 224.5 121.5536 57.78761 224.5 124.0145 58.90063 224.5 120.8904 57.21406 223.5 128.3721 51.794 223.5 132.9514 50.98654 223.5 133.6012 54.67135 223.5 129.0218 55.47881 223.5 128.2803 51.50871 223.5 132.9401 50.68707 223.5 120.1489 53.24396 223.5 120.7987 56.92877 223.5 116.2193 57.73624 223.5 115.5696 54.05142 223.5 133.6929 54.95664 223.5 127.8883 51.06274 223.5 130.4277 42.27274 223.5 133.1559 50.13392 223.5 134.1905 48.8317 223.5 133.3671 49.92129 223.5 115.4778 53.76614 223.5 120.1376 52.94449 223.5 128.115 51.25878 223.5 133.8583 55.20657 223.5 107.3464 55.50139 223.5 134.0849 55.40261 223.5 133.01 50.39566 223.5 107.3351 55.20192 223.5 120.2075 52.65308 223.5 127.6171 50.93516 223.5 107.9962 59.1862 223.5 120.8904 57.21406 223.5 134.3561 55.53019 223.5 127.7827 57.63365 223.5 128.6061 56.54406 223.5 108.0879 59.47149 223.5 121.0558 57.464 223.5 141.7658 53.81227 223.5 141.8357 53.52085 223.5 135.5025 56.27243 223.5 136.817 56.6432 223.5 126.4707 50.19292 223.5 129.0332 55.77828 223.5 128.8173 56.33143 223.5 108.48 59.91746 223.5 108.7511 60.04504 223.5 108.2533 59.72143 223.5 107.5509 54.64877 223.5 107.405 54.91051 223.5 108.5856 53.34655 223.5 107.7621 54.43614 223.5 115.0858 53.32017 223.5 114.8146 53.19259 223.5 121.2825 57.66003 223.5 141.6198 54.074 223.5 140.5852 55.37622 223.5 141.4086 54.28664 223.5 135.2989 48.03372 223.5 135.2544 40.18409 223.5 136.5934 47.59846 223.5 128.9633 56.06969 223.5 116.2307 58.0357 223.5 121.5536 57.78761 223.5 122.7001 58.52986 223.5 116.0148 58.58886 223.5 116.1608 58.32712 223.5 113.8718 60.68905 223.5 114.9802 59.89107 223.5 115.3125 53.5162 223.5 114.9608 44.99998 223.5 120.3534 52.39134 223.5 121.388 51.08913 223.5 120.5646 52.17871 223.5 138.1823 56.60946 223.5 126.6744 58.43162 223.5 115.8036 58.80149 223.5 112.5773 61.12431 223.5 111.212 61.15806 223.5 104.5333 43.76582 223.5 109.6939 52.54858 223.5 109.7109 44.68809 223.5 125.1563 49.82215 223.5 125.3904 43.784 223.5 139.4768 56.1742 223.5 141.8243 53.22138 223.5 141.1746 49.53657 223.5 124.0145 58.90063 223.5 125.3798 58.86688 223.5 109.8976 60.78728 223.5 110.9884 52.11332 223.5 113.6682 52.45034 223.5 120.2112 44.69724 223.5 123.7909 49.85589 223.5 140.6908 48.80532 223.5 140.4196 48.67773 223.5 112.3538 52.07957 223.5 122.4964 50.29115 223.5 140.9175 49.00135 223.5 141.0828 49.25128 223.5 139.2732 47.93549 223.5 137.9588 47.56472 223.5 139.8043 37.54658 225.9539 137.1286 39.1832 225.9539 134.7318 40.44324 225.9539 131.4441 41.88784 225.9539 128.0517 43.06568 225.9539 132.5614 41.43181 225.9539 127.7695 43.15019 225.9539 124.5762 43.96927 225.9539 121.0397 44.59285 225.9539 122.8142 44.31635 225.9539 117.4647 44.93245 225.9539 113.8741 44.98591 225.9539 117.7588 44.91535 225.9539 110.2906 44.75289 225.9539 112.6682 44.93954 196.0461 107.6073 44.38861 196.0461 110.2906 44.75289 196.0461 113.8741 44.98591 196.0461 117.4647 44.93245 196.0461 112.6682 44.93954 196.0461 117.7588 44.91535 196.0461 121.0397 44.59285 196.0461 124.5762 43.96927 196.0461 122.8142 44.31635 196.0461 128.0517 43.06568 196.0461 131.4441 41.88784 196.0461 127.7695 43.15019 196.0461 134.7318 40.44324 196.0461 132.5614 41.43181 225.9539 106.7371 44.23487 225.9539 107.6073 44.38861 225.9539 103.2362 43.43516 225.9539 140.91 36.79224 225.9539 137.8938 38.7411 225.9185 141.4151 36.43133 225.8353 141.827 36.12911 225.484 142.6883 35.47336 225.5995 142.4667 35.6452 225.0904 143.2565 35.02245 224.9081 143.4608 34.85656 224.3884 143.9193 34.47717 223.59 144.3939 34.07339 224.1085 144.1114 34.31509 223.4803 144.4434 34.03068 223.024 144.6167 33.87995 222.4058 144.7756 33.74036 215.477 145.1567 33.40019 216.3864 145.1288 33.42537 211.5443 145.2181 33.34469 219.7722 144.9796 33.55929 207.6101 145.1833 33.37617 203.7042 145.0534 33.49314 205.6136 145.1288 33.42537 200.2384 144.8583 33.66721 221.7616 144.8583 33.66721 222.3305 144.7894 33.72816 199.5214 144.7611 33.75313 198.976 144.6167 33.87995 198.3029 144.3425 34.11772 197.8915 144.1114 34.31509 197.3271 143.6878 34.67001 197.0919 143.4608 34.85656 196.6551 142.9151 35.29518 196.2357 142.0627 35.95286 196.516 142.6883 35.47336 196.0815 141.4151 36.43133 196.1647 141.827 36.12911 196.0461 137.8938 38.7411 196.0461 137.1286 39.1832 196.0461 140.91 36.79224 196.0461 103.2362 43.43516 196.0461 106.7371 44.23487 196.0815 102.6382 43.26875 196.1647 102.1478 43.12563 196.516 101.1141 42.804 196.4004 101.3811 42.8897 196.9095 100.426 42.57462 197.0919 100.1772 42.48864 197.6116 99.61666 42.28892 197.8915 99.38067 42.20233 198.5196 98.97145 42.04861 198.976 98.75704 41.96625 199.6694 98.5428 41.88269 206.523 98.08551 41.70012 205.6136 98.12034 41.71423 210.4557 98.00888 41.66895 202.2278 98.30638 41.78903 214.3899 98.05231 41.68664 218.2958 98.21434 41.75214 216.3864 98.12034 41.71423 221.7616 98.45722 41.84897 200.2384 98.45722 41.84897 222.4786 98.57792 41.89648 223.024 98.75704 41.96625 223.697 99.09604 42.09589 224.1085 99.38067 42.20233 224.6729 99.90013 42.39096 224.9081 100.1772 42.48864 225.3449 100.8401 42.71413 225.7643 101.8659 43.04064 225.484 101.1141 42.804 225.9185 102.6382 43.26875 225.8353 102.1478 43.12563 197.9539 109.7109 44.68809 197.9539 106.7371 44.23487 197.9539 113.8741 44.98591 197.9539 110.2906 44.75289 197.9539 114.9608 44.99998 197.9539 120.2112 44.69724 197.9539 117.4647 44.93245 197.9539 124.5762 43.96927 197.9539 121.0397 44.59285 197.9539 125.3904 43.784 197.9539 128.0517 43.06568 197.9539 130.4277 42.27274 197.9539 134.7318 40.44324 197.9539 131.4441 41.88784 197.9539 135.2544 40.18409 197.9539 137.8938 38.7411 197.9539 139.8043 37.54658 224.0461 139.8043 37.54658 224.0461 140.91 36.79224 223.9692 141.5064 36.36499 223.7421 142.0685 35.94852 223.375 142.5693 35.56589 223.3206 142.6258 35.52204 222.8927 142.971 35.25084 222.7708 143.0455 35.1916 222.266 143.2718 35.01011 222.5931 143.1389 35.11697 221.9357 143.3597 34.93893 221.5914 143.4054 34.90172 222.1117 143.3186 34.97223 218.2957 143.599 34.7433 216.3006 143.6815 34.67525 214.3899 143.736 34.6301 210.4896 143.7728 34.59954 206.5917 143.7099 34.6517 205.6994 143.6815 34.67525 202.2276 143.5212 34.80721 200.4086 143.4054 34.90172 200.2846 143.394 34.91102 200.0643 143.3597 34.93893 199.7718 143.2842 35.00002 199.7339 143.2718 35.01011 199.3855 143.1285 35.12528 199.1073 142.971 35.25084 198.9596 142.868 35.3323 198.5292 142.4611 35.64952 198.2579 142.0685 35.94852 198.625 142.5693 35.56589 198.1772 141.9109 36.06664 197.9539 140.91 36.79224 198.0308 141.5064 36.36499 224.0461 135.2544 40.18409 224.0461 137.8938 38.7411 224.0461 131.4441 41.88784 224.0461 134.7318 40.44324 224.0461 130.4277 42.27274 224.0461 125.3904 43.784 224.0461 128.0517 43.06568 224.0461 121.0397 44.59285 224.0461 124.5762 43.96927 224.0461 120.2112 44.69724 224.0461 117.4647 44.93245 224.0461 114.9608 44.99998 224.0461 110.2906 44.75289 224.0461 113.8741 44.98591 224.0461 109.7109 44.68809 224.0461 106.7371 44.23487 224.0461 104.5333 43.76582 197.9539 104.5333 43.76582 197.9539 103.2362 43.43516 198.0308 102.5297 43.23762 198.2579 101.859 43.03853 198.625 101.2576 42.85027 198.6794 101.1895 42.82838 199.1073 100.7723 42.69161 199.2292 100.6821 42.66142 199.7339 100.4074 42.56825 199.4069 100.5688 42.62323 200.0643 100.3004 42.53143 200.4086 100.2447 42.51213 199.8883 100.3504 42.54867 203.7043 100.0086 42.42946 205.6994 99.90787 42.39372 207.6101 99.84121 42.36993 211.5104 99.79618 42.35379 215.4083 99.87308 42.38132 216.3006 99.90787 42.39372 219.7724 100.1036 42.4629 221.5914 100.2447 42.51213 221.7154 100.2586 42.51696 221.9357 100.3004 42.53143 222.2281 100.3922 42.56304 222.266 100.4074 42.56825 222.6145 100.5814 42.6275 222.8927 100.7723 42.69161 223.0404 100.897 42.73294 223.4708 101.3879 42.89184 223.7421 101.859 43.03853 223.375 101.2576 42.85027 223.8227 102.0475 43.09563 224.0461 103.2362 43.43516 223.9692 102.5297 43.23762 196.1669 138.0226 38.95898 196.1592 141.049 36.98956 196.1669 134.8428 40.67069 196.1669 131.5366 42.12341 196.1669 128.1251 43.30788 196.1669 124.6301 44.21654 196.1669 121.0737 44.84363 196.1669 117.4786 45.18514 196.1669 113.8677 45.23891 196.1669 110.2641 45.00458 196.1669 106.6906 44.48365 196.3453 141.1726 37.16499 196.3532 134.9347 40.85905 196.3532 131.6132 42.3185 196.3532 128.1859 43.50844 196.3532 124.6747 44.42132 196.3532 121.1018 45.05131 196.3532 117.4901 45.39441 196.3532 113.8625 45.44842 196.3532 110.2422 45.213 196.3532 106.6521 44.68966 196.3453 103.117 43.87521 196.5843 141.261 37.29056 196.3532 138.1292 39.1394 196.5904 135.0003 40.9935 196.5904 131.6678 42.45775 196.5904 128.2293 43.65162 196.5904 124.7065 44.5675 196.5904 121.1219 45.19956 196.5904 117.4983 45.54379 196.5904 113.8588 45.59798 196.5904 110.2265 45.36178 196.5904 106.6247 44.83672 196.5843 103.0769 44.02345 196.8567 141.3072 37.35613 196.5904 138.2053 39.2682 196.8598 135.0344 41.06348 196.8598 131.6963 42.53023 196.8598 128.2519 43.72613 196.8598 124.7231 44.64358 196.8598 121.1323 45.27672 196.8598 117.5025 45.62154 196.8598 113.8568 45.67581 196.8598 110.2184 45.43922 196.8598 106.6104 44.91326 196.8567 103.0559 44.10086 197.1408 141.3074 37.35642 196.8598 138.2449 39.33523 197.1402 135.0344 41.06348 197.1402 131.6963 42.53023 197.1402 128.2519 43.72613 197.1402 124.7231 44.64358 197.1402 121.1323 45.27672 197.1402 117.5025 45.62154 197.1402 113.8568 45.67581 197.1402 110.2184 45.43922 197.1402 106.6104 44.91326 197.1408 103.0558 44.1012 197.1402 138.2449 39.33523 197.4096 138.2053 39.2682 197.4096 135.0003 40.9935 197.4096 131.6678 42.45775 197.4096 128.2293 43.65162 197.4096 124.7065 44.5675 197.4096 121.1219 45.19956 197.4096 117.4983 45.54379 197.4096 113.8588 45.59798 197.4096 110.2265 45.36178 197.4096 106.6247 44.83672 197.4135 103.0766 44.02444 197.4135 141.2616 37.2914 197.6468 138.1292 39.1394 197.6468 134.9347 40.85905 197.6468 131.6132 42.3185 197.6468 128.1859 43.50844 197.6468 124.6747 44.42132 197.6468 121.1018 45.05131 197.6468 117.4901 45.39441 197.6468 113.8625 45.44842 197.6468 110.2422 45.213 197.6468 106.6521 44.68966 197.6528 103.1166 43.87677 197.6528 141.1735 37.16631 197.8331 138.0226 38.95898 197.8331 134.8428 40.67069 197.8331 131.5366 42.12341 197.8331 128.1251 43.30788 197.8331 124.6301 44.21654 197.8331 121.0737 44.84363 197.8331 117.4786 45.18514 197.8331 113.8677 45.23891 197.8331 110.2641 45.00458 197.8331 106.6906 44.48365 197.8395 103.1726 43.6701 197.8395 141.0502 36.99126 196.1592 103.1731 43.6681 196.4544 142.0461 36.5309 196.6881 142.0978 36.68511 196.9549 142.1001 36.78427 197.2333 142.0529 36.82032 197.5008 141.96 36.79036 197.7378 141.8274 36.69549 197.7355 141.8289 36.69681 197.9199 141.6689 36.54578 197.9186 141.6703 36.54725 196.2729 141.9493 36.33412 196.2766 141.9521 36.33928 196.7843 142.8682 35.9057 196.791 142.8696 35.91251 196.6204 142.7997 35.69557 197.0024 142.8855 36.08896 197.2518 142.8467 36.22216 197.5122 142.755 36.29443 197.7624 142.6178 36.2999 197.982 142.4464 36.23813 197.9862 142.4425 36.23611 198.1558 142.2509 36.11126 198.1532 142.2546 36.11413 196.6153 142.7962 35.68729 197.327 143.6064 35.31919 197.3341 143.6071 35.32847 197.1837 143.5602 35.09051 197.5191 143.5926 35.53069 197.7389 143.5163 35.69708 197.9685 143.384 35.80475 198.1891 143.2063 35.8449 198.3825 142.9979 35.81423 198.3876 142.9915 35.81228 198.5332 142.7758 35.71525 198.5365 142.7698 35.71171 197.1784 143.5568 35.07989 198.0775 144.2251 34.80946 198.083 144.2248 34.81966 197.964 144.1993 34.56084 198.2298 144.1821 35.04839 198.4042 144.0712 35.24686 198.5865 143.9014 35.38856 198.7616 143.6867 35.46184 198.9152 143.4447 35.46071 198.9194 143.4371 35.45948 199.0346 143.1953 35.38523 199.0374 143.1883 35.38203 197.9598 144.1967 34.54969 198.4676 144.4756 34.31961 198.5646 144.4927 34.58876 199.0936 144.7044 34.40593 198.6898 144.4349 34.83998 199.1888 144.6365 34.66754 198.833 144.3067 35.05261 199.2976 144.4965 34.89258 198.9823 144.1188 35.20921 199.4111 144.2957 35.06256 199.1257 143.8867 35.2969 199.52 144.0506 35.16355 199.2512 143.6293 35.30849 199.6153 143.7814 35.18723 199.3485 143.3678 35.24302 199.6892 143.5102 35.13167 199.0198 144.6944 34.12923 199.6217 144.8507 33.99709 199.6675 144.8534 34.27994 200.2646 144.932 34.20705 199.7265 144.7762 34.54949 200.2859 144.8517 34.48179 199.794 144.6254 34.7836 200.3104 144.6966 34.72207 199.8644 144.4134 34.96306 200.3359 144.4795 34.90809 199.9319 144.1576 35.07312 200.3604 144.2183 35.02453 199.991 143.8789 35.10476 200.3818 143.9345 35.0618 200.0368 143.6004 35.05537 200.3985 143.6515 35.01681 200.2481 144.931 33.92049 205.627 145.2058 33.96909 205.6377 145.1263 34.24417 205.6501 144.972 34.48533 205.6629 144.7557 34.67277 205.6752 144.495 34.79111 205.686 144.2114 34.83062 205.6943 143.9282 34.78807 205.6186 145.204 33.68268 211 144.018 34.71006 211 144.3014 34.75249 216.3056 143.9282 34.78807 216.314 144.2114 34.83062 211 144.5852 34.71282 216.3248 144.495 34.79111 211 144.8461 34.5943 216.3371 144.7557 34.67277 211 145.0626 34.40667 216.3499 144.972 34.48533 211 145.2171 34.16533 216.3623 145.1263 34.24417 211 145.2968 33.89009 216.373 145.2058 33.96909 211 145.2951 33.60356 216.3814 145.204 33.68268 224.1669 138.0226 38.95898 224.1669 141.0558 36.99916 224.1605 141.0502 36.99126 224.1669 134.8428 40.67069 224.1669 131.5366 42.12341 224.1669 128.1251 43.30788 224.1669 124.6301 44.21654 224.1669 121.0737 44.84363 224.1669 117.4786 45.18514 224.1669 113.8677 45.23891 224.1669 110.2641 45.00458 224.1669 106.6906 44.48365 224.1605 103.1726 43.6701 224.1669 103.17 43.67943 224.3532 138.1292 39.1394 224.3472 141.1735 37.16631 224.3532 141.1764 37.17051 224.3532 134.9347 40.85905 224.3532 131.6132 42.3185 224.3532 128.1859 43.50844 224.3532 124.6747 44.42132 224.3532 121.1018 45.05131 224.3532 117.4901 45.39441 224.3532 113.8625 45.44842 224.3532 110.2422 45.213 224.3532 106.6521 44.68966 224.3472 103.1166 43.87677 224.5904 138.2053 39.2682 224.5904 135.0003 40.9935 224.5904 131.6678 42.45775 224.5904 128.2293 43.65162 224.5904 124.7065 44.5675 224.5904 121.1219 45.19956 224.5904 117.4983 45.54379 224.5904 113.8588 45.59798 224.5904 110.2265 45.36178 224.5904 106.6247 44.83672 224.3532 103.1153 43.88172 224.5865 103.0766 44.02444 224.5904 141.2626 37.29283 224.8598 138.2449 39.33523 224.8598 135.0344 41.06348 224.8598 131.6963 42.53023 224.8598 128.2519 43.72613 224.8598 124.7231 44.64358 224.8598 121.1323 45.27672 224.8598 117.5025 45.62154 224.8598 113.8568 45.67581 224.8598 110.2184 45.43922 224.8598 106.6104 44.91326 224.5904 103.0762 44.02613 224.8598 103.0558 44.10128 224.8598 141.3074 37.35649 225.1402 138.2449 39.33523 225.1402 135.0344 41.06348 225.1402 131.6963 42.53023 225.1402 128.2519 43.72613 225.1402 124.7231 44.64358 225.1402 121.1323 45.27672 225.1402 117.5025 45.62154 225.1402 113.8568 45.67581 225.1402 110.2184 45.43922 225.1402 106.6104 44.91326 225.1402 103.0558 44.10128 225.1402 141.3074 37.35649 225.4096 138.2053 39.2682 225.4096 135.0003 40.9935 225.4096 131.6678 42.45775 225.4096 128.2293 43.65162 225.4096 124.7065 44.5675 225.4096 121.1219 45.19956 225.4096 117.4983 45.54379 225.4096 113.8588 45.59798 225.4096 110.2265 45.36178 225.4096 106.6247 44.83672 225.4096 103.0762 44.02613 225.4096 141.2626 37.29283 225.6468 138.1292 39.1394 225.6468 134.9347 40.85905 225.6468 131.6132 42.3185 225.6468 128.1859 43.50844 225.6468 124.6747 44.42132 225.6468 121.1018 45.05131 225.6468 117.4901 45.39441 225.6468 113.8625 45.44842 225.6468 110.2422 45.213 225.6468 106.6521 44.68966 225.6468 103.1153 43.88172 225.8331 138.0226 38.95898 225.8331 141.0558 36.99916 225.6547 141.1726 37.16499 225.8331 134.8428 40.67069 225.8331 131.5366 42.12341 225.8331 128.1251 43.30788 225.8331 124.6301 44.21654 225.8331 121.0737 44.84363 225.8331 117.4786 45.18514 225.8331 113.8677 45.23891 225.8331 110.2641 45.00458 225.8331 106.6906 44.48365 225.6547 103.117 43.87521 225.8331 103.17 43.67943 225.8408 141.049 36.98956 225.8408 103.1731 43.6681 225.6468 141.1764 37.17051 224.2622 141.8274 36.69549 224.0814 141.6703 36.54725 224.0801 141.6689 36.54578 224.4959 141.9586 36.78955 224.7624 142.0518 36.82036 225.0402 142.0997 36.78546 225.3069 142.0983 36.68764 225.5409 142.0478 36.5348 225.7234 141.9521 36.33928 225.5456 142.0461 36.5309 225.7271 141.9493 36.33412 224.0138 142.4425 36.23611 223.8468 142.2546 36.11413 224.2319 142.6141 36.29921 224.481 142.7519 36.29544 224.7408 142.8448 36.22511 224.9902 142.8851 36.09392 225.209 142.8696 35.91251 224.9976 142.8855 36.08896 225.3796 142.7997 35.69557 225.2157 142.8682 35.9057 223.8441 142.2509 36.11126 225.3847 142.7962 35.68729 223.6124 142.9915 35.81228 223.4668 142.7758 35.71525 223.8043 143.2002 35.84501 224.0238 143.3786 35.80725 224.2528 143.5125 35.70206 224.4728 143.5908 35.53802 224.6659 143.6071 35.32847 224.4809 143.5926 35.53069 224.8162 143.5602 35.09051 224.673 143.6064 35.31919 223.4635 142.7698 35.71171 224.8216 143.5568 35.07989 223.0806 143.4371 35.45948 222.9626 143.1883 35.38203 223.233 143.6791 35.463 223.4072 143.8946 35.39228 223.5892 144.0659 35.25311 223.7638 144.1791 35.05689 223.917 144.2248 34.81966 224.036 144.1993 34.56084 222.3093 143.5037 35.12933 222.6515 143.3678 35.24302 222.7488 143.6293 35.30849 222.3826 143.7747 35.18681 222.8743 143.8867 35.2969 222.4775 144.0443 35.16505 223.0177 144.1188 35.20921 222.5861 144.2902 35.06584 223.167 144.3067 35.05261 222.6997 144.4923 34.89734 223.3102 144.4349 34.83998 222.8087 144.634 34.67337 223.4354 144.4927 34.58876 222.9044 144.7037 34.41233 223.5324 144.4756 34.31961 222.9788 144.6955 34.13568 221.6013 143.6475 35.01555 221.9632 143.6004 35.05537 222.009 143.8789 35.10476 221.6178 143.9298 35.06172 222.0681 144.1576 35.07312 221.6392 144.2133 35.02593 222.1356 144.4134 34.96306 221.6636 144.4747 34.91113 222.206 144.6254 34.7836 221.6891 144.6925 34.72674 222.2734 144.7762 34.54949 221.7135 144.8489 34.48789 222.3325 144.8534 34.27994 221.7349 144.931 34.2142 222.3783 144.8507 33.99709 221.7516 144.9321 33.92814 197.7378 102.3411 43.65798 197.9186 102.438 43.46496 197.5041 102.25 43.79124 197.2376 102.1729 43.8521 196.9598 102.1159 43.83567 196.6931 102.0838 43.74328 196.2766 102.1021 43.36592 196.4544 102.0793 43.57814 197.9199 102.4388 43.4631 196.2729 102.1029 43.36011 196.4591 102.079 43.58237 197.9862 101.606 43.43668 198.1532 101.7408 43.25782 197.768 101.4663 43.55467 197.519 101.3355 43.59828 197.2592 101.2242 43.56394 197.0098 101.1414 43.45446 196.791 101.0939 43.2787 197.0024 101.1394 43.44992 196.6204 101.0855 43.05091 196.7843 101.093 43.27179 196.6153 101.0859 43.04194 198.1558 101.7433 43.25384 198.3876 100.9451 43.22621 198.5332 101.1146 43.06124 198.1957 100.7602 43.32832 197.9762 100.5796 43.35388 197.7472 100.4179 43.30081 197.5272 100.2882 43.17343 197.3341 100.2012 42.98211 197.5191 100.284 43.16716 197.1837 100.1639 42.74244 197.327 100.1986 42.97315 197.1784 100.1635 42.7313 198.5365 101.1191 43.05586 198.9194 100.4058 43.04706 199.0374 100.613 42.88919 198.767 100.1795 43.13315 198.5928 99.95282 43.1404 198.4108 99.74423 43.06822 198.2362 99.5708 42.92254 198.083 99.44671 42.71525 197.964 99.38214 42.46332 197.9598 99.38082 42.45193 199.6907 100.2302 42.75961 199.3485 100.3968 42.81996 199.2512 100.1735 42.97091 199.6174 99.9952 42.90633 199.1257 99.92768 43.04806 199.5225 99.73445 42.97807 198.9823 99.67951 43.04506 199.4139 99.46942 42.96896 198.833 99.44941 42.96217 199.3003 99.22187 42.87974 198.6898 99.25627 42.80618 199.1912 99.0121 42.71775 198.5646 99.11593 42.58992 199.0956 98.85739 42.49627 198.4676 99.03995 42.33115 199.0198 98.76926 42.22707 199.0212 98.77041 42.23352 200.3987 100.0562 42.70186 200.0368 100.114 42.7232 199.991 99.8692 42.86487 200.3822 99.80673 42.8418 199.9319 99.59658 42.93043 200.3608 99.52809 42.90514 199.8644 99.31855 42.91449 200.3364 99.24319 42.88666 199.794 99.05796 42.81837 200.3109 98.97541 42.78789 199.7265 98.83617 42.64995 200.2865 98.74675 42.61694 199.6675 98.67141 42.42307 200.2651 98.57598 42.38784 199.6217 98.57721 42.15636 200.2484 98.47713 42.11941 205.6862 99.46934 42.71861 205.6945 99.72042 42.57974 205.6755 99.18924 42.7804 205.6632 98.90319 42.76004 205.6503 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51 1 3 38 1 4 37 1 5 35 2 6 52 2 7 36 2 8 29 3 9 31 3 10 30 3 11 28 4 12 45 4 13 29 4 14 45 5 15 27 5 16 26 5 17 33 6 18 31 6 19 34 6 20 46 7 21 45 7 22 25 7 23 2273 11 33 20 11 34 2274 11 35 50 12 36 44 12 37 43 12 38 18 14 42 49 14 43 19 14 44 2288 17 51 2292 17 52 2293 17 53 2284 19 57 31 19 58 2265 19 59 22 24 72 48 24 73 47 24 74 36 28 84 52 28 85 37 28 86 51 29 87 40 29 88 39 29 89 37 30 90 52 30 91 51 30 92 51 31 93 39 31 94 38 31 95 35 32 96 34 32 97 52 32 98 28 33 99 27 33 100 45 33 101 33 34 102 32 34 103 31 34 104 26 35 105 25 35 106 45 35 107 24 36 108 46 36 109 25 36 110 49 43 129 2274 43 130 20 43 131 2273 44 132 48 44 133 20 44 134 43 45 135 42 45 136 50 45 137 41 46 138 40 46 139 42 46 140 50 47 141 17 47 142 44 47 143 42 48 144 40 48 145 50 48 146 20 51 153 19 51 154 49 51 155 18 52 156 17 52 157 49 52 158 2294 17 171 2295 17 172 2288 17 173 2296 17 174 2297 17 175 2288 17 176 2298 17 177 2299 17 178 2288 17 179 2300 17 180 2301 17 181 2288 17 182 2302 17 183 2303 17 184 2288 17 185 2285 17 186 2286 17 187 2288 17 188 2287 17 189 2288 17 190 2286 17 191 2289 17 192 2290 17 193 2288 17 194 2291 17 195 2292 17 196 2288 17 197 2293 17 198 2294 17 199 2288 17 200 2297 17 201 2298 17 202 2288 17 203 2301 17 204 2302 17 205 2288 17 206 2285 17 207 2288 17 208 2303 17 209 2290 17 210 2291 17 211 2288 17 212 2295 17 213 2296 17 214 2288 17 215 2300 17 216 2288 17 217 2299 17 218 2275 58 222 2274 58 223 49 58 224 49 59 225 17 59 226 2275 59 227 40 60 228 51 60 229 2279 60 230 34 61 231 31 61 232 2284 61 233 48 62 234 2273 62 235 2272 62 236 48 63 237 2272 63 238 47 63 239 2275 64 240 17 64 241 2276 64 242 2276 65 243 17 65 244 50 65 245 47 66 246 2272 66 247 2271 66 248 47 67 249 2271 67 250 2270 67 251 2277 68 252 2276 68 253 50 68 254 2277 69 255 50 69 256 40 69 257 46 70 258 47 70 259 2270 70 260 46 71 261 2270 71 262 2269 71 263 2278 72 264 2277 72 265 40 72 266 2279 73 267 2278 73 268 40 73 269 45 74 270 46 74 271 2268 74 272 46 75 273 2269 75 274 2268 75 275 2280 76 276 2279 76 277 51 76 278 2280 77 279 51 77 280 52 77 281 45 78 282 2268 78 283 2267 78 284 45 79 285 2267 79 286 2266 79 287 2281 80 288 2280 80 289 52 80 290 2282 81 291 2281 81 292 52 81 293 29 82 294 45 82 295 2266 82 296 29 83 297 2266 83 298 2265 83 299 2282 84 300 52 84 301 34 84 302 2283 85 303 2282 85 304 34 85 305 31 86 306 29 86 307 2265 86 308 2284 87 309 2283 87 310 34 87 311 46 98 342 24 98 343 23 98 344 21 99 345 20 99 346 48 99 347 47 100 348 46 100 349 23 100 350 21 101 351 48 101 352 22 101 353 47 102 354 23 102 355 22 102 356

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37 0 0 13 0 1 36 0 2 42 8 24 8 8 25 9 8 26 36 9 27 14 9 28 35 9 29 40 10 30 41 10 31 9 10 32 1 13 39 27 13 40 0 13 41 3 15 45 25 15 46 26 15 47 24 16 48 4 16 49 23 16 50 22 18 54 6 18 55 21 18 56 17 20 60 18 20 61 7 20 62 16 21 63 33 21 64 15 21 65 19 22 66 6 22 67 7 22 68 23 23 69 5 23 70 22 23 71 38 25 75 11 25 76 12 25 77 31 26 78 32 26 79 16 26 80 37 27 81 12 27 82 13 27 83 42 37 111 43 37 112 8 37 113 15 38 114 35 38 115 14 38 116 36 39 117 13 39 118 14 39 119 9 40 120 10 40 121 40 40 122 39 41 123 40 41 124 10 41 125 41 42 126 42 42 127 9 42 128 28 49 147 0 49 148 27 49 149 1 50 150 26 50 151 27 50 152 26 53 159 1 53 160 3 53 161 3 54 162 2 54 163 25 54 164 24 55 165 25 55 166 2 55 167 2 56 168 4 56 169 24 56 170 22 57 219 5 57 220 6 57 221 7 88 312 8 88 313 44 88 314 44 89 315 17 89 316 7 89 317 8 90 318 43 90 319 44 90 320 35 91 321 15 91 322 34 91 323 16 92 324 32 92 325 33 92 326 33 93 327 34 93 328 15 93 329 19 94 330 20 94 331 6 94 332 21 95 333 6 95 334 20 95 335 7 96 336 18 96 337 19 96 338 23 97 339 4 97 340 5 97 341 12 103 357 37 103 358 38 103 359 38 104 360 39 104 361 10 104 362 10 105 363 11 105 364 38 105 365 16 106 366 0 106 367 30 106 368 28 107 369 29 107 370 0 107 371 30 108 372 31 108 373 16 108 374 0 109 375 29 109 376 30 109 377 905 110 378 923 110 379 884 110 380 1499 111 381 1498 111 382 1532 111 383 840 112 384 839 112 385 882 112 386 1856 113 387 1886 113 388 1899 113 389 1990 114 390 1991 114 391 2013 114 392 1636 115 393 1654 115 394 1671 115 395 1752 116 396 1795 116 397 1816 116 398 475 117 399 524 117 400 476 117 401 842 118 402 843 118 403 819 118 404 1421 119 405 1378 119 406 1380 119 407 1397 120 408 1400 120 409 1398 120 410 1498 121 411 1515 121 412 1496 121 413 1229 122 414 1171 122 415 1168 122 416 528 123 417 481 123 418 480 123 419 2156 124 420 2110 124 421 2111 124 422 1172 125 423 1230 125 424 1231 125 425 143 126 426 177 126 427 144 126 428 1442 127 429 1373 127 430 1372 127 431 65 128 432 0 128 433 16 128 434 512 129 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315 995 1309 316 996 1291 316 997 1292 316 998 1370 317 999 1369 317 1000 1368 317 1001 1914 318 1002 1831 318 1003 1851 318 1004 1311 319 1005 1297 319 1006 1312 319 1007 245 320 1008 264 320 1009 286 320 1010 1295 321 1011 1246 321 1012 1321 321 1013 1727 322 1014 1826 322 1015 1728 322 1016 1943 323 1017 1944 323 1018 1942 323 1019 1534 324 1020 1502 324 1021 1501 324 1022 2195 325 1023 2048 325 1024 2046 325 1025 1460 326 1026 1470 326 1027 1461 326 1028 516 327 1029 534 327 1030 533 327 1031 669 328 1032 646 328 1033 644 328 1034 1506 329 1035 1483 329 1036 1520 329 1037 1787 330 1038 1786 330 1039 1785 330 1040 255 331 1041 296 331 1042 275 331 1043 632 332 1044 652 332 1045 653 332 1046 109 333 1047 70 333 1048 95 333 1049 2152 334 1050 2153 334 1051 2154 334 1052 1836 335 1053 1760 335 1054 1758 335 1055 1237 336 1056 1236 336 1057 1303 336 1058 1139 337 1059 1190 337 1060 1138 337 1061 1517 338 1062 1503 338 1063 1504 338 1064 1266 339 1065 1329 339 1066 1267 339 1067 1375 340 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364 1141 1734 364 1142 2008 365 1143 2007 365 1144 1977 365 1145 1694 366 1146 1664 366 1147 1706 366 1148 154 367 1149 114 367 1150 140 367 1151 2261 368 1152 2207 368 1153 2206 368 1154 945 369 1155 900 369 1156 901 369 1157 1129 370 1158 1128 370 1159 1238 370 1160 1414 371 1161 1365 371 1162 1367 371 1163 1628 372 1164 1596 372 1165 1597 372 1166 1728 373 1167 1672 373 1168 1693 373 1169 1135 374 1170 1133 374 1171 1136 374 1172 1503 375 1173 1536 375 1174 1504 375 1175 1127 376 1176 1126 376 1177 1124 376 1178 2209 377 1179 2210 377 1180 2145 377 1181 671 378 1182 628 378 1183 647 378 1184 544 379 1185 543 379 1186 504 379 1187 1533 380 1188 1532 380 1189 1616 380 1190 521 381 1191 473 381 1192 520 381 1193 2239 382 1194 2240 382 1195 2255 382 1196 423 383 1197 392 383 1198 393 383 1199 230 384 1200 293 384 1201 231 384 1202 1424 385 1203 1336 385 1204 1335 385 1205 497 386 1206 329 386 1207 328 386 1208 235 387 1209 298 387 1210 257 387 1211 1766 388 1212 1820 388 1213 1800 388 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981 3000 129 981 3001 141 981 3002 1233 982 3003 1234 982 3004 1117 982 3005 1945 983 3006 1946 983 3007 1985 983 3008 908 984 3009 874 984 3010 909 984 3011 672 985 3012 661 985 3013 660 985 3014 877 986 3015 848 986 3016 876 986 3017 2220 987 3018 2243 987 3019 2244 987 3020 1794 988 3021 1718 988 3022 1815 988 3023 1972 989 3024 2004 989 3025 1971 989 3026 1621 990 3027 1584 990 3028 1587 990 3029 560 991 3030 544 991 3031 545 991 3032 508 992 3033 542 992 3034 541 992 3035 124 993 3036 123 993 3037 87 993 3038 2111 994 3039 2112 994 3040 2113 994 3041 1325 995 3042 1261 995 3043 1260 995 3044 1943 996 3045 2022 996 3046 2023 996 3047 932 997 3048 887 997 3049 917 997 3050 993 998 3051 964 998 3052 992 998 3053 982 999 3054 999 999 3055 1000 999 3056 996 1000 3057 975 1000 3058 973 1000 3059 967 1001 3060 977 1001 3061 998 1001 3062 2227 1002 3063 2232 1002 3064 2226 1002 3065 1740 1003 3066 1813 1003 3067 1790 1003 3068 1326 1004 3069 1325 1004 3070 1414 1004 3071 1035 1005 3072 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3140 1182 1028 3141 1108 1028 3142 1181 1028 3143 2155 1029 3144 2154 1029 3145 2153 1029 3146 1077 1030 3147 1012 1030 3148 1068 1030 3149 1662 1031 3150 1651 1031 3151 1707 1031 3152 1097 1032 3153 1196 1032 3154 1197 1032 3155 1957 1033 3156 1959 1033 3157 1992 1033 3158 1995 1034 3159 1961 1034 3160 1962 1034 3161 2181 1035 3162 2127 1035 3163 2163 1035 3164 1024 1036 3165 1107 1036 3166 1108 1036 3167 2133 1037 3168 2132 1037 3169 2135 1037 3170 1840 1038 3171 1772 1038 3172 1770 1038 3173 1413 1039 3174 1365 1039 3175 1414 1039 3176 1642 1040 3177 1641 1040 3178 1668 1040 3179 882 1041 3180 917 1041 3181 887 1041 3182 1201 1042 3183 1164 1042 3184 1202 1042 3185 773 1043 3186 747 1043 3187 772 1043 3188 1131 1044 3189 1130 1044 3190 1129 1044 3191 749 1045 3192 788 1045 3193 787 1045 3194 525 1046 3195 477 1046 3196 476 1046 3197 793 1047 3198 770 1047 3199 794 1047 3200 1474 1048 3201 1475 1048 3202 1521 1048 3203 1842 1049 3204 1774 1049 3205 1841 1049 3206 1194 1050 3207 1148 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3275 1107 1073 3276 1180 1073 3277 1108 1073 3278 1988 1074 3279 1989 1074 3280 2012 1074 3281 1550 1075 3282 1651 1075 3283 1551 1075 3284 1306 1076 3285 1267 1076 3286 1307 1076 3287 1899 1077 3288 1849 1077 3289 1848 1077 3290 1311 1078 3291 1296 1078 3292 1297 1078 3293 467 1079 3294 465 1079 3295 466 1079 3296 1052 1080 3297 1098 1080 3298 1053 1080 3299 2113 1081 3300 2157 1081 3301 2176 1081 3302 1225 1082 3303 1224 1082 3304 1310 1082 3305 250 1083 3306 227 1083 3307 249 1083 3308 1318 1084 3309 1319 1084 3310 1220 1084 3311 1427 1085 3312 1391 1085 3313 1428 1085 3314 1671 1086 3315 1654 1086 3316 1700 1086 3317 1022 1087 3318 1056 1087 3319 1075 1087 3320 2179 1088 3321 2178 1088 3322 2120 1088 3323 1394 1089 3324 1430 1089 3325 1429 1089 3326 1729 1090 3327 1812 1090 3328 1730 1090 3329 1612 1091 3330 1613 1091 3331 1530 1091 3332 493 1092 3333 470 1092 3334 471 1092 3335 1419 1093 3336 1331 1093 3337 1330 1093 3338 1531 1094 3339 1495 1094 3340 1530 1094 3341 1189 1095 3342 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164 1118 3411 194 1118 3412 153 1118 3413 2136 1119 3414 2134 1119 3415 2137 1119 3416 1200 1120 3417 1101 1120 3418 1199 1120 3419 1032 1121 3420 1031 1121 3421 1111 1121 3422 516 1122 3423 469 1122 3424 468 1122 3425 291 1123 3426 290 1123 3427 269 1123 3428 2127 1124 3429 2162 1124 3430 2125 1124 3431 2074 1125 3432 2075 1125 3433 2101 1125 3434 1482 1126 3435 1445 1126 3436 1444 1126 3437 1626 1127 3438 1596 1127 3439 1627 1127 3440 1573 1128 3441 1575 1128 3442 1615 1128 3443 903 1129 3444 946 1129 3445 902 1129 3446 488 1130 3447 461 1130 3448 462 1130 3449 1601 1131 3450 1548 1131 3451 1549 1131 3452 1162 1132 3453 1200 1132 3454 1199 1132 3455 1609 1133 3456 1562 1133 3457 1564 1133 3458 1453 1134 3459 1397 1134 3460 1452 1134 3461 1996 1135 3462 1997 1135 3463 1917 1135 3464 1666 1136 3465 1665 1136 3466 1646 1136 3467 145 1137 3468 125 1137 3469 144 1137 3470 1622 1138 3471 1587 1138 3472 1589 1138 3473 1488 1139 3474 1489 1139 3475 1490 1139 3476 619 1140 3477 593 1140 3478 599 1140 3479 948 1141 3480 922 1141 3481 923 1141 3482 1414 1142 3483 1324 1142 3484 1413 1142 3485 1539 1143 3486 1599 1143 3487 1600 1143 3488 1484 1144 3489 1485 1144 3490 1539 1144 3491 1626 1145 3492 1627 1145 3493 1537 1145 3494 1530 1146 3495 1614 1146 3496 1531 1146 3497 2219 1147 3498 2218 1147 3499 2242 1147 3500 1929 1148 3501 1975 1148 3502 1974 1148 3503 1853 1149 3504 1891 1149 3505 1904 1149 3506 1734 1150 3507 1733 1150 3508 1732 1150 3509 1858 1151 3510 1910 1151 3511 1894 1151 3512 155 1152 3513 156 1152 3514 113 1152 3515 142 1153 3516 179 1153 3517 178 1153 3518 417 1154 3519 444 1154 3520 416 1154 3521 1428 1155 3522 1339 1155 3523 1338 1155 3524 742 1156 3525 715 1156 3526 741 1156 3527 876 1157 3528 908 1157 3529 907 1157 3530 1698 1158 3531 1697 1158 3532 1676 1158 3533 182 1159 3534 161 1159 3535 160 1159 3536 1709 1160 3537 1697 1160 3538 1698 1160 3539 1696 1161 3540 1695 1161 3541 1674 1161 3542 1350 1162 3543 1349 1162 3544 1410 1162 3545 2038 1163 3546 2018 1163 3547 1931 1163 3548 72 1164 3549 98 1164 3550 71 1164 3551 114 1165 3552 58 1165 3553 106 1165 3554 1929 1166 3555 1927 1166 3556 1928 1166 3557 852 1167 3558 854 1167 3559 832 1167 3560 2177 1168 3561 2116 1168 3562 2158 1168 3563 2113 1169 3564 2175 1169 3565 2111 1169 3566 1763 1170 3567 1761 1170 3568 1762 1170 3569 1737 1171 3570 1736 1171 3571 1735 1171 3572 1738 1172 3573 1741 1172 3574 1740 1172 3575 1759 1173 3576 1758 1173 3577 1760 1173 3578 1745 1174 3579 1743 1174 3580 1744 1174 3581 1750 1175 3582 1749 1175 3583 1751 1175 3584 1770 1176 3585 1769 1176 3586 1767 1176 3587 1351 1177 3588 1353 1177 3589 1352 1177 3590 1748 1178 3591 1747 1178 3592 1746 1178 3593 1776 1179 3594 1775 1179 3595 1774 1179 3596 1542 1180 3597 1487 1180 3598 1541 1180 3599 1766 1181 3600 1765 1181 3601 1764 1181 3602 1755 1182 3603 1754 1182 3604 1753 1182 3605 1778 1183 3606 1777 1183 3607 1779 1183 3608 1810 1184 3609 1733 1184 3610 1788 1184 3611 1789 1185 3612 1739 1185 3613 1790 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72 1208 3682 99 1208 3683 1006 1209 3684 980 1209 3685 1005 1209 3686 1443 1210 3687 1377 1210 3688 1375 1210 3689 109 1211 3690 132 1211 3691 162 1211 3692 432 1212 3693 401 1212 3694 402 1212 3695 1047 1213 3696 1073 1213 3697 1072 1213 3698 1892 1214 3699 1905 1214 3700 1875 1214 3701 6 1215 3702 70 1215 3703 7 1215 3704 1076 1216 3705 1023 1216 3706 1066 1216 3707 1910 1217 3708 1893 1217 3709 1894 1217 3710 1896 1218 3711 1862 1218 3712 1913 1218 3713 1897 1219 3714 1846 1219 3715 1913 1219 3716 1914 1220 3717 1889 1220 3718 1901 1220 3719 2027 1221 3720 2026 1221 3721 2091 1221 3722 1950 1222 3723 1949 1222 3724 1951 1222 3725 1116 1223 3726 1181 1223 3727 1180 1223 3728 498 1224 3729 476 1224 3730 332 1224 3731 1952 1225 3732 1951 1225 3733 1954 1225 3734 1998 1226 3735 1967 1226 3736 1969 1226 3737 847 1227 3738 825 1227 3739 824 1227 3740 688 1228 3741 670 1228 3742 669 1228 3743 1603 1229 3744 1554 1229 3745 1604 1229 3746 668 1230 3747 644 1230 3748 643 1230 3749 1996 1231 3750 1966 1231 3751 1997 1231 3752 2038 1232 3753 2037 1232 3754 2083 1232 3755 666 1233 3756 645 1233 3757 642 1233 3758 1276 1234 3759 1295 1234 3760 1275 1234 3761 796 1235 3762 769 1235 3763 768 1235 3764 746 1236 3765 772 1236 3766 747 1236 3767 2246 1237 3768 2226 1237 3769 2247 1237 3770 1449 1238 3771 1389 1238 3772 1448 1238 3773 1850 1239 3774 1855 1239 3775 1900 1239 3776 2261 1240 3777 2234 1240 3778 2235 1240 3779 2069 1241 3780 2070 1241 3781 2099 1241 3782 1123 1242 3783 1183 1242 3784 1122 1242 3785 1501 1243 3786 1502 1243 3787 1516 1243 3788 253 1244 3789 233 1244 3790 232 1244 3791 1550 1245 3792 1549 1245 3793 1548 1245 3794 2056 1246 3795 2076 1246 3796 2054 1246 3797 571 1247 3798 559 1247 3799 532 1247 3800 1031 1248 3801 1068 1248 3802 1030 1248 3803 2022 1249 3804 2021 1249 3805 2086 1249 3806 1351 1250 3807 1354 1250 3808 1405 1250 3809 247 1251 3810 288 1251 3811 266 1251 3812 845 1252 3813 872 1252 3814 846 1252 3815 499 1253 3816 332 1253 3817 477 1253 3818 2005 1254 3819 1866 1254 3820 1865 1254 3821 2160 1255 3822 2159 1255 3823 2123 1255 3824 1451 1256 3825 1393 1256 3826 1450 1256 3827 1019 1257 3828 1053 1257 3829 1054 1257 3830 2049 1258 3831 2183 1258 3832 2050 1258 3833 1355 1259 3834 1352 1259 3835 1458 1259 3836 1430 1260 3837 1431 1260 3838 1340 1260 3839 854 1261 3840 899 1261 3841 898 1261 3842 1844 1262 3843 1781 1262 3844 1779 1262 3845 1158 1263 3846 1160 1263 3847 1161 1263 3848 2042 1264 3849 2077 1264 3850 2078 1264 3851 2189 1265 3852 2190 1265 3853 2059 1265 3854 120 1266 3855 147 1266 3856 148 1266 3857 1933 1267 3858 1934 1267 3859 1935 1267 3860 1204 1268 3861 1105 1268 3862 1104 1268 3863 2171 1269 3864 2109 1269 3865 2170 1269 3866 1446 1270 3867 1385 1270 3868 1382 1270 3869 145 1271 3870 175 1271 3871 174 1271 3872 470 1272 3873 516 1272 3874 517 1272 3875 236 1273 3876 299 1273 3877 258 1273 3878 2166 1274 3879 2197 1274 3880 2185 1274 3881 2150 1275 3882 2151 1275 3883 2152 1275 3884 2248 1276 3885 2204 1276 3886 2260 1276 3887 1409 1277 3888 1356 1277 3889 1358 1277 3890 1948 1278 3891 1950 1278 3892 1987 1278 3893 678 1279 3894 704 1279 3895 705 1279 3896 734 1280 3897 760 1280 3898 735 1280 3899 720 1281 3900 690 1281 3901 719 1281 3902 1655 1282 3903 1634 1282 3904 1633 1282 3905 1066 1283 3906 1024 1283 3907 1067 1283 3908 485 1284 3909 459 1284 3910 460 1284 3911 1362 1285 3912 1412 1285 3913 1411 1285 3914 1986 1286 3915 1987 1286 3916 2011 1286 3917 1705 1287 3918 1704 1287 3919 1691 1287 3920 762 1288 3921 774 1288 3922 802 1288 3923 922 1289 3924 904 1289 3925 884 1289 3926 905 1290 3927 906 1290 3928 923 1290 3929 922 1291 3930 884 1291 3931 923 1291 3932 1532 1292 3933 1533 1292 3934 1499 1292 3935 1499 1293 3936 1500 1293 3937 1498 1293 3938 1533 1294 3939 1501 1294 3940 1499 1294 3941 863 1295 3942 882 1295 3943 839 1295 3944 864 1296 3945 840 1296 3946 882 1296 3947 1856 1297 3948 1898 1297 3949 1886 1297 3950 2014 1298 3951 2013 1298 3952 1992 1298 3953 2003 1299 3954 1990 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4023 1228 1322 4024 1229 1322 4025 480 1323 4026 527 1323 4027 528 1323 4028 528 1324 4029 529 1324 4030 481 1324 4031 2156 1325 4032 2155 1325 4033 2110 1325 4034 1232 1326 4035 1176 1326 4036 1175 1326 4037 1175 1327 4038 1172 1327 4039 1231 1327 4040 1231 1328 4041 1232 1328 4042 1175 1328 4043 176 1329 4044 144 1329 4045 177 1329 4046 143 1330 4047 141 1330 4048 178 1330 4049 178 1331 4050 177 1331 4051 143 1331 4052 1370 1332 4053 1441 1332 4054 1372 1332 4055 1442 1333 4056 1443 1333 4057 1373 1333 4058 1375 1334 4059 1373 1334 4060 1443 1334 4061 1372 1335 4062 1441 1335 4063 1442 1335 4064 75 1336 4065 58 1336 4066 0 1336 4067 59 1337 4068 73 1337 4069 57 1337 4070 57 1338 4071 3 1338 4072 1 1338 4073 73 1339 4074 60 1339 4075 57 1339 4076 58 1340 4077 74 1340 4078 1 1340 4079 62 1341 4080 67 1341 4081 54 1341 4082 53 1342 4083 54 1342 4084 63 1342 4085 15 1343 4086 14 1343 4087 53 1343 4088 54 1344 4089 67 1344 4090 63 1344 4091 59 1345 4092 57 1345 4093 1 1345 4094 74 1346 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43 113 92 113 93 113 44 114 95 114 96 114 44 115 94 115 95 115 52 116 97 116 98 116 52 117 96 117 97 117 58 118 53 118 99 118 53 119 98 119 99 119 99 120 60 120 58 120 98 121 53 121 52 121 96 122 52 122 44 122 94 123 44 123 43 123 92 124 43 124 42 124 90 125 42 125 46 125 88 126 46 126 48 126 86 127 48 127 57 127 84 128 57 128 56 128 81 129 56 129 55 129 79 130 82 130 55 130 77 131 78 131 54 131 75 132 76 132 51 132 73 133 74 133 47 133 71 134 72 134 45 134 69 135 70 135 41 135 67 136 68 136 40 136 65 137 66 137 50 137 63 138 64 138 49 138 61 139 62 139 59 139 100 140 101 140 102 140 103 141 104 141 91 141 91 142 90 142 103 142 104 143 92 143 91 143 104 144 93 144 92 144 103 145 89 145 88 145 88 146 105 146 103 146 88 147 87 147 105 147 87 148 86 148 105 148 100 149 76 149 75 149 100 150 77 150 76 150 100 151 78 151 77 151 100 152 79 152 78 152 100 153 82 153 79 153 106 154 80 154 82 154 106 155 105 155 85 155 85 156 84 156 106 156 84 157 83 157 106 157 83 158 81 158 106 158 81 159 80 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203 136 203 137 204 136 204 138 204 139 205 138 205 140 205 141 206 140 206 142 206 143 207 142 207 144 207 145 208 144 208 146 208 147 209 146 209 148 209 149 210 148 210 150 210 151 211 150 211 152 211 153 212 152 212 154 212 155 213 154 213 156 213 157 214 156 214 158 214 159 215 158 215 160 215 161 216 160 216 162 216 163 217 162 217 164 217 165 218 164 218 166 218 167 219 166 219 168 219 169 220 168 220 170 220 171 221 170 221 172 221 173 222 172 222 174 222 175 223 174 223 102 223 102 224 101 224 175 224 174 225 175 225 173 225 172 226 173 226 171 226 170 227 171 227 169 227 168 228 169 228 167 228 166 229 167 229 165 229 164 230 165 230 163 230 162 231 163 231 161 231 160 232 161 232 159 232 158 233 159 233 157 233 156 234 157 234 155 234 154 235 155 235 153 235 152 236 153 236 151 236 150 237 151 237 149 237 148 238 149 238 147 238 146 239 147 239 145 239 144 240 145 240 143 240 142 241 143 241 141 241 140 242 141 242 139 242 138 243 139 243 137 243 136 244 137 244 135 244 134 245 135 245 133 245 132 246 133 246 131 246 130 247 131 247 129 247 128 248 129 248 127 248 126 249 127 249 125 249 124 250 125 250 123 250 122 251 123 251 121 251 120 252 121 252 119 252 118 253 119 253 117 253 116 254 117 254 115 254 114 255 115 255 112 255 111 256 110 256 176 256 177 257 176 257 178 257 179 258 178 258 180 258 181 259 180 259 182 259 182 260 183 260 181 260 180 258 181 258 179 258 178 261 179 261 177 261 176 262 177 262 111 262 184 263 185 263 183 263 183 264 182 264 184 264 186 265 187 265 185 265 185 266 184 266 186 266 188 267 27 267 29 267 186 268 33 268 35 268 29 269 186 269 188 269 188 270 23 270 25 270 189 271 17 271 19 271 189 272 187 272 9 272 186 273 31 273 33 273 25 274 27 274 188 274 188 275 19 275 21 275 189 276 15 276 17 276 189 277 11 277 13 277 187 278 7 278 9 278 187 279 0 279 3 279 29 280 31 280 186 280 19 281 188 281 189 281 9 282 11 282 189 282 3 283 5 283 187 283 21 284 23 284 188 284 5 285 7 285 187 285 13 286 15 286 189 286 35 287 37 287 186 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329 262 330 264 330 261 330 260 331 262 331 259 331 258 332 260 332 257 332 256 333 258 333 255 333 254 334 256 334 253 334 252 335 254 335 251 335 250 336 252 336 249 336 248 337 250 337 247 337 246 338 248 338 245 338 244 339 246 339 243 339 242 340 244 340 241 340 240 341 242 341 239 341 238 342 240 342 237 342 236 343 238 343 235 343 234 344 236 344 233 344 232 345 234 345 231 345 230 346 232 346 229 346 228 347 230 347 227 347 226 348 228 348 225 348 224 349 226 349 223 349 222 350 224 350 221 350 220 351 222 351 219 351 218 352 220 352 217 352 216 353 218 353 215 353 214 354 216 354 213 354 212 355 214 355 211 355 210 356 212 356 209 356 208 357 210 357 207 357 206 358 208 358 205 358 204 359 206 359 203 359 202 360 204 360 201 360 200 361 202 361 199 361 198 362 200 362 197 362 196 363 198 363 195 363 194 364 196 364 193 364 192 365 194 365 191 365 188 366 192 366 190 366 112 367 266 367 267 367 268 368 269 368 270 368 270 369 271 369 268 369 269 370 272 370 270 370 269 371 273 371 272 371 268 372 274 372 275 372 275 373 276 373 268 373 275 374 277 374 276 374 277 375 278 375 276 375 267 376 279 376 280 376 267 377 281 377 279 377 267 378 282 378 281 378 267 379 283 379 282 379 267 380 266 380 284 380 284 381 283 381 267 381 266 382 276 382 285 382 285 383 286 383 266 383 286 384 287 384 266 384 287 385 288 385 266 385 288 386 289 386 266 386 289 387 284 387 266 387 290 388 291 388 292 388 291 389 293 389 292 389 291 390 294 390 293 390 291 391 280 391 294 391 290 392 295 392 296 392 290 393 297 393 295 393 296 394 298 394 290 394 296 395 299 395 298 395 300 396 301 396 273 396 300 397 302 397 301 397 300 398 303 398 302 398 300 399 304 399 303 399 300 400 305 400 304 400 300 401 306 401 305 401 300 402 307 402 308 402 308 403 306 403 300 403 307 404 298 404 309 404 309 405 310 405 307 405 310 406 311 406 307 406 311 407 312 407 307 407 312 408 313 408 307 408 313 409 314 409 307 409 314 410 315 410 307 410 315 411 308 411 307 411 273 412 269 412 300 412 299 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426 246 426 126 426 124 426 122 442 120 442 250 442 120 443 118 443 252 443 202 444 172 444 170 444 206 445 168 445 166 445 216 426 214 426 158 426 220 446 218 446 154 446 228 447 144 447 142 447 140 448 232 448 230 448 138 449 234 449 232 449 136 450 236 450 234 450 134 451 238 451 236 451 240 452 132 452 130 452 124 453 122 453 248 453 252 426 250 426 120 426 118 454 116 454 254 454 116 455 114 455 256 455 114 456 113 456 258 456 105 457 106 457 196 457 265 458 322 458 320 458 204 426 170 426 168 426 218 426 216 426 156 426 230 459 142 459 140 459 234 460 138 460 136 460 238 426 134 426 132 426 250 461 248 461 122 461 256 462 254 462 116 462 258 463 256 463 114 463 106 464 198 464 196 464 106 465 202 465 200 465 188 466 178 466 176 466 188 467 182 467 180 467 265 426 307 426 322 426 212 426 210 426 162 426 232 426 140 426 138 426 242 468 130 468 128 468 113 469 267 469 258 469 200 470 198 470 106 470 103 426 105 426 194 426 180 471 178 471 188 471 188 472 186 472 184 472 265 473 317 473 324 473 320 426 318 426 265 426 262 426 260 426 291 426 224 474 222 474 150 474 254 475 252 475 118 475 194 426 192 426 103 426 104 426 103 426 192 426 184 426 182 426 188 426 318 476 317 476 265 476 298 426 265 426 264 426 290 426 264 426 262 426 260 477 258 477 267 477 236 478 136 478 134 478 192 426 188 426 104 426 176 426 110 426 188 426 265 426 298 426 307 426 291 426 290 426 262 426 174 479 172 479 202 479 110 426 104 426 188 426 264 426 290 426 298 426 196 480 194 480 105 480 267 426 291 426 260 426 324 426 325 426 265 426 165 481 205 481 207 481 175 481 101 481 195 481 121 482 247 482 249 482 123 481 245 481 247 481 125 483 127 483 243 483 141 484 143 484 227 484 143 481 145 481 225 481 153 481 217 481 219 481 155 481 215 481 217 481 161 481 209 481 211 481 163 481 207 481 209 481 207 485 163 485 165 485 167 486 169 486 203 486 173 487 175 487 197 487 195 488 197 488 175 488 247 481 121 481 123 481 245 481 123 481 125 481 127 481 129 481 241 481 129 481 131 481 239 481 131 481 133 481 237 481 133 489 135 489 235 489 225 481 227 481 143 481 147 490 223 490 225 490 149 491 221 491 223 491 151 492 219 492 221 492 217 481 153 481 155 481 157 493 213 493 215 493 159 481 211 481 213 481 209 481 161 481 163 481 203 494 205 494 167 494 169 495 171 495 201 495 171 496 173 496 199 496 101 497 100 497 195 497 189 481 179 481 181 481 189 498 111 498 177 498 251 499 112 499 115 499 243 481 245 481 125 481 239 481 241 481 129 481 235 481 237 481 133 481 135 481 137 481 233 481 137 481 139 481 231 481 139 481 141 481 229 481 225 500 145 500 147 500 221 481 149 481 151 481 215 501 155 501 157 501 211 481 159 481 161 481 201 502 203 502 169 502 197 503 199 503 173 503 189 504 183 504 185 504 177 481 179 481 189 481 251 505 266 505 112 505 119 506 249 506 251 506 241 481 243 481 127 481 233 481 235 481 135 481 229 481 231 481 139 481 223 507 147 507 149 507 213 481 157 481 159 481 199 508 201 508 171 508 259 509 268 509 276 509 263 481 319 481 321 481 181 481 183 481 189 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537 330 537 325 538 338 538 339 538 326 539 340 539 331 539 334 540 328 540 263 540 263 541 265 541 341 541 330 542 335 542 265 542 339 543 342 543 325 543 326 544 343 544 344 544 329 545 340 545 326 545 341 546 345 546 263 546 265 547 346 547 347 547 342 548 337 548 325 548 344 549 325 549 326 549 345 550 333 550 263 550 336 551 346 551 265 551 332 552 343 552 326 552 344 553 338 553 325 553 347 554 341 554 265 554 338 555 344 555 348 555 339 556 338 556 349 556 342 557 339 557 350 557 337 558 342 558 351 558 330 559 337 559 352 559 335 560 330 560 353 560 336 561 335 561 354 561 346 562 336 562 355 562 347 563 346 563 356 563 341 564 347 564 357 564 345 565 341 565 358 565 333 566 345 566 359 566 334 567 333 567 360 567 328 568 334 568 361 568 329 569 328 569 362 569 340 570 329 570 363 570 331 571 340 571 364 571 332 572 331 572 365 572 343 573 332 573 366 573 344 574 343 574 367 574 367 575 348 575 344 575 366 576 367 576 343 576 365 577 366 577 332 577 364 578 365 578 331 578 363 579 364 579 340 579 362 580 363 580 329 580 361 581 362 581 328 581 360 582 361 582 334 582 359 583 360 583 333 583 358 584 359 584 345 584 357 585 358 585 341 585 356 586 357 586 347 586 355 587 356 587 346 587 354 588 355 588 336 588 353 589 354 589 335 589 352 590 353 590 330 590 351 591 352 591 337 591 350 592 351 592 342 592 349 593 350 593 339 593 348 594 349 594 338 594 368 595 352 595 351 595 369 596 353 596 352 596 352 597 368 597 369 597 370 598 354 598 353 598 353 599 369 599 370 599 371 600 355 600 354 600 372 601 373 601 364 601 373 602 374 602 365 602 375 603 350 603 349 603 376 604 351 604 350 604 354 605 370 605 371 605 371 606 377 606 356 606 378 607 372 607 363 607 365 608 364 608 373 608 350 609 375 609 376 609 356 610 355 610 371 610 377 611 379 611 357 611 379 612 380 612 358 612 381 613 378 613 362 613 364 614 363 614 372 614 351 615 376 615 368 615 358 616 357 616 379 616 382 617 359 617 358 617 383 618 360 618 359 618 381 619 361 619 360 619 363 620 362 620 378 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662 277 662 378 663 274 663 271 663 378 664 275 664 274 664 372 665 270 665 272 665 372 666 271 666 270 666 373 667 273 667 301 667 373 668 272 668 273 668 374 669 302 669 303 669 374 670 301 670 302 670 384 671 304 671 305 671 384 672 303 672 304 672 386 673 385 673 306 673 385 674 305 674 306 674 306 675 308 675 386 675 305 676 385 676 384 676 303 677 384 677 374 677 301 678 374 678 373 678 272 679 373 679 372 679 271 680 372 680 378 680 275 681 378 681 381 681 278 682 381 682 383 682 286 683 383 683 382 683 288 684 382 684 380 684 283 685 284 685 380 685 281 686 282 686 379 686 280 687 279 687 377 687 293 688 294 688 371 688 297 689 292 689 370 689 296 690 295 690 369 690 309 691 299 691 368 691 311 692 310 692 376 692 313 693 312 693 375 693 315 694 314 694 387 694

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58.39826 84.17741 18.37843 58.85368 84.64219 18.85961 58.72914 84.64219 15.34039 58.72914 84.17741 15.82157 58.85368 83.879 17.77211 58.93364 87.87513 16.42789 57.86288 87.57672 15.82157 57.94284 85.87706 14.925 58.39826 87.11193 15.34039 58.06738 85.22785 15.03145 58.57222 86.52629 15.03145 58.22431 59.62803 29.6 65.43167 60.49404 27.84039 65.19963 63.72698 30.27211 64.33336 63.82981 29.6 64.30581 59.73085 28.92789 65.40412 60.02926 28.32157 65.32416 63.72698 28.92789 64.33336 63.42857 28.32157 64.41333 61.72891 31.775 64.86874 62.37813 31.66855 64.6948 60.02926 30.87843 65.32416 60.49404 31.35961 65.19963 61.07971 27.53145 65.0427 62.96379 31.35961 64.53786 63.42857 30.87843 64.41333 59.73085 30.27211 65.40412 61.07971 31.66855 65.0427 61.72891 27.425 64.86874 62.37813 27.53145 64.6948 62.96379 27.84039 64.53786 98.1174 27.53145 55.11848 97.46817 27.425 55.29243 99.16783 28.32157 54.83701 98.70304 27.84039 54.96155 99.46624 28.92789 54.75705 99.46624 30.27211 54.75705 96.81896 31.66855 55.46639 95.76852 30.87843 55.74785 96.2333 31.35961 55.62332 99.56906 29.6 54.7295 97.46817 31.775 55.29243 98.1174 31.66855 55.11848 98.70304 31.35961 54.96155 99.16783 30.87843 54.83701 95.36729 29.6 55.85536 95.47011 30.27211 55.82781 95.47011 28.92789 55.82781 96.2333 27.84039 55.62332 95.76852 28.32157 55.74785 96.81896 27.53145 55.46639 61.91009 27.425 65.54489 61.26088 27.53145 65.71884 60.67522 27.84039 65.87577 60.21043 28.32157 66.00032 59.91202 28.92789 66.08027 59.8092 29.6 66.10782 59.91202 30.27211 66.08027 60.21043 30.87843 66.00032 60.67522 31.35961 65.87577 61.26088 31.66855 65.71884 61.91009 31.775 65.54489 62.5593 31.66855 65.37094 63.14496 31.35961 65.21401 63.60974 30.87843 65.08946 63.90815 30.27211 65.00951 64.01098 29.6 64.98196 63.90815 28.92789 65.00951 63.60974 28.32157 65.08946 63.14496 27.84039 65.21401 62.5593 27.53145 65.37094 73.5012 14.925 62.43906 72.85199 15.03145 62.61301 72.26633 15.34039 62.76994 71.80155 15.82157 62.89448 71.50315 16.42789 62.97444 71.40032 17.1 63.00199 71.50315 17.77211 62.97444 71.80155 18.37843 62.89448 72.26633 18.85961 62.76994 72.85199 19.16855 62.61301 73.5012 19.275 62.43906 74.15042 19.16855 62.2651 74.73607 18.85961 62.10818 75.20085 18.37843 61.98364 75.49927 17.77211 61.90368 75.60209 17.1 61.87612 75.49927 16.42789 61.90368 75.20085 15.82157 61.98364 74.73607 15.34039 62.10818 74.15042 15.03145 62.2651 73.5012 20.925 62.43906 72.85199 21.03145 62.61301 72.26633 21.34039 62.76994 71.80155 21.82157 62.89448 71.50315 22.42789 62.97444 71.40032 23.1 63.00199 71.50315 23.77211 62.97444 71.80155 24.37843 62.89448 72.26633 24.85961 62.76994 72.85199 25.16855 62.61301 73.5012 25.275 62.43906 74.15042 25.16855 62.2651 74.73607 24.85961 62.10818 75.20085 24.37843 61.98364 75.49927 23.77211 61.90368 75.60209 23.1 61.87612 75.49927 22.42789 61.90368 75.20085 21.82157 61.98364 74.73607 21.34039 62.10818 74.15042 21.03145 62.2651 86.05825 14.925 59.07441 85.40903 15.03145 59.24837 84.82337 15.34039 59.40529 84.35858 15.82157 59.52983 84.06018 16.42789 59.60979 83.95735 17.1 59.63734 84.06018 17.77211 59.60979 84.35858 18.37843 59.52983 84.82337 18.85961 59.40529 85.40903 19.16855 59.24837 86.05825 19.275 59.07441 86.70745 19.16855 58.90045 87.29312 18.85961 58.74353 87.75789 18.37843 58.61899 88.0563 17.77211 58.53903 88.15913 17.1 58.51148 88.0563 16.42789 58.53903 87.75789 15.82157 58.61899 87.29312 15.34039 58.74353 86.70745 15.03145 58.90045 86.05825 20.925 59.07441 85.40903 21.03145 59.24837 84.82337 21.34039 59.40529 84.35858 21.82157 59.52983 84.06018 22.42789 59.60979 83.95735 23.1 59.63734 84.06018 23.77211 59.60979 84.35858 24.37843 59.52983 84.82337 24.85961 59.40529 85.40903 25.16855 59.24837 86.05825 25.275 59.07441 86.70745 25.16855 58.90045 87.29312 24.85961 58.74353 87.75789 24.37843 58.61899 88.0563 23.77211 58.53903 88.15913 23.1 58.51148 88.0563 22.42789 58.53903 87.75789 21.82157 58.61899 87.29312 21.34039 58.74353 86.70745 21.03145 58.90045 97.64936 27.425 55.96858 97.00014 27.53145 56.14253 96.41448 27.84039 56.29946 95.94969 28.32157 56.424 95.65129 28.92789 56.50396 95.54846 29.6 56.53151 95.65129 30.27211 56.50396 95.94969 30.87843 56.424 96.41448 31.35961 56.29946 97.00014 31.66855 56.14253 97.64936 31.775 55.96858 98.29856 31.66855 55.79463 98.88422 31.35961 55.6377 99.349 30.87843 55.51316 99.64741 30.27211 55.4332 99.75024 29.6 55.40565 99.64741 28.92789 55.4332 99.349 28.32157 55.51316 98.88422 27.84039 55.6377 98.29856 27.53145 55.79463 97.72699 32.075 56.25836 98.46575 31.95387 56.06041 99.13219 31.60232 55.88184 99.66108 31.05477 55.74012 100.0007 30.36482 55.64913 100.1177 29.6 55.61778 100.0007 28.83518 55.64913 99.66108 28.14523 55.74012 99.13219 27.59768 55.88184 98.46575 27.24614 56.06041 97.72699 27.125 56.25836 96.98825 27.24614 56.45631 96.3218 27.59768 56.63488 95.7929 28.14523 56.77659 95.45333 28.83518 56.86758 95.33633 29.6 56.89894 95.45333 30.36482 56.86758 95.7929 31.05477 56.77659 96.3218 31.60232 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28.13054 56.21785 99.27581 27.57746 56.361 98.60263 27.22236 56.54137 86.13588 25.575 59.36419 86.87464 25.45387 59.16624 87.54108 25.10232 58.98767 88.06997 24.55477 58.84595 88.40954 23.86482 58.75496 88.52655 23.1 58.72361 88.40954 22.33518 58.75496 88.06997 21.64523 58.84595 87.54108 21.09768 58.98767 86.87464 20.74614 59.16624 86.13588 20.625 59.36419 85.39714 20.74614 59.56214 84.73069 21.09768 59.74071 84.20179 21.64523 59.88243 83.86222 22.33518 59.97341 83.74522 23.1 60.00476 83.86222 23.86482 59.97341 84.20179 24.55477 59.88243 84.73069 25.10232 59.74071 85.39714 25.45387 59.56214 88.43251 23.87254 58.74881 88.5507 23.1 58.71714 88.43251 22.32746 58.74881 88.08951 21.63054 58.84072 86.88211 20.72236 59.16424 86.13588 20.6 59.36419 85.38967 20.72236 59.56414 88.08951 24.56946 58.84072 87.55529 21.07746 58.98386 84.71649 21.07746 59.74451 83.83926 22.32746 59.97957 83.72107 23.1 60.01123 83.83926 23.87254 59.97957 84.18226 21.63054 59.88766 84.18226 24.56946 59.88766 84.71649 25.12254 59.74451 86.13588 25.6 59.36419 86.88211 25.47764 59.16424 87.55529 25.12254 58.98386 85.38967 25.47764 59.56414 86.26529 20.6 59.84715 85.51908 20.72236 60.0471 84.8459 21.07746 60.22748 84.31167 21.63054 60.37062 83.96868 22.32746 60.46253 83.85048 23.1 60.4942 83.96868 23.87254 60.46253 84.31167 24.56946 60.37062 84.8459 25.12254 60.22748 85.51908 25.47764 60.0471 86.26529 25.6 59.84715 87.01152 25.47764 59.6472 87.6847 25.12254 59.46683 88.21892 24.56946 59.32368 88.56192 23.87254 59.23177 88.68011 23.1 59.2001 88.56192 22.32746 59.23177 88.21892 21.63054 59.32368 87.6847 21.07746 59.46683 87.01152 20.72236 59.6472 86.13588 19.575 59.36419 86.87464 19.45387 59.16624 87.54108 19.10232 58.98767 88.06997 18.55477 58.84595 88.40954 17.86482 58.75496 88.52655 17.1 58.72361 88.40954 16.33518 58.75496 88.06997 15.64523 58.84595 87.54108 15.09768 58.98767 86.87464 14.74613 59.16624 86.13588 14.625 59.36419 85.39714 14.74613 59.56214 84.73069 15.09768 59.74071 84.20179 15.64523 59.88243 83.86222 16.33518 59.97341 83.74522 17.1 60.00476 83.86222 17.86482 59.97341 84.20179 18.55477 59.88243 84.73069 19.10232 59.74071 85.39714 19.45387 59.56214 86.13588 19.6 59.36419 86.88211 19.47764 59.16424 87.55529 19.12254 58.98386 88.08951 18.56946 58.84072 88.43251 17.87254 58.74881 88.5507 17.1 58.71714 88.43251 16.32746 58.74881 88.08951 15.63054 58.84072 87.55529 15.07746 58.98386 86.88211 14.72236 59.16424 86.13588 14.6 59.36419 85.38967 14.72236 59.56414 84.18226 15.63054 59.88766 83.83926 16.32746 59.97957 83.72107 17.1 60.01123 84.71649 15.07746 59.74451 83.83926 17.87254 59.97957 84.18226 18.56946 59.88766 84.71649 19.12254 59.74451 85.38967 19.47764 59.56414 86.26529 14.6 59.84715 85.51908 14.72236 60.0471 84.8459 15.07746 60.22748 84.31167 15.63054 60.37062 83.96868 16.32746 60.46253 83.85048 17.1 60.4942 83.96868 17.87254 60.46253 84.31167 18.56946 60.37062 84.8459 19.12254 60.22748 85.51908 19.47764 60.0471 86.26529 19.6 59.84715 87.01152 19.47764 59.6472 87.6847 19.12254 59.46683 88.21892 18.56946 59.32368 88.56192 17.87254 59.23177 88.68011 17.1 59.2001 88.56192 16.32746 59.23177 88.21892 15.63054 59.32368 87.6847 15.07746 59.46683 87.01152 14.72236 59.6472 73.57884 25.575 62.72883 74.31761 25.45387 62.53089 74.98405 25.10232 62.35231 75.51293 24.55477 62.2106 75.8525 23.86482 62.11961 75.96952 23.1 62.08826 75.8525 22.33518 62.11961 75.51293 21.64523 62.2106 74.98405 21.09768 62.35231 74.31761 20.74614 62.53089 73.57884 20.625 62.72883 72.84009 20.74614 62.92678 72.17366 21.09768 63.10536 71.64476 21.64523 63.24707 71.30519 22.33518 63.33806 71.18818 23.1 63.36941 71.30519 23.86482 63.33806 71.64476 24.55477 63.24707 72.17366 25.10232 63.10536 72.84009 25.45387 62.92678 75.87548 23.87254 62.11346 75.99366 23.1 62.08179 75.87548 22.32746 62.11346 71.28222 22.32746 63.34421 71.16403 23.1 63.37588 71.28222 23.87254 63.34421 75.53247 21.63054 62.20536 74.32508 20.72236 62.52889 73.57884 20.6 62.72883 72.83263 20.72236 62.92878 71.62522 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15.09768 62.35231 74.31761 14.74613 62.53089 73.57884 14.625 62.72883 72.84009 14.74613 62.92678 72.17366 15.09768 63.10536 71.64476 15.64523 63.24707 71.30519 16.33518 63.33806 71.18818 17.1 63.36941 71.30519 17.86482 63.33806 71.64476 18.55477 63.24707 72.17366 19.10232 63.10536 72.84009 19.45387 62.92678 74.32508 14.72236 62.52889 73.57884 14.6 62.72883 72.83263 14.72236 62.92878 71.62522 15.63054 63.25231 71.28222 16.32746 63.34421 71.16403 17.1 63.37588 72.15946 15.07746 63.10916 71.28222 17.87254 63.34421 73.57884 19.6 62.72883 74.32508 19.47764 62.52889 74.99824 19.12254 62.34851 75.53247 18.56946 62.20536 75.87548 17.87254 62.11346 75.99366 17.1 62.08179 75.87548 16.32746 62.11346 71.62522 18.56946 63.25231 75.53247 15.63054 62.20536 72.15946 19.12254 63.10916 72.83263 19.47764 62.92878 74.99824 15.07746 62.34851 73.70825 14.6 63.2118 72.96205 14.72236 63.41175 72.28887 15.07746 63.59212 71.75463 15.63054 63.73527 71.41163 16.32746 63.82718 71.29344 17.1 63.85884 71.41163 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47.62216 64.71749 39.42443 47.86648 60.94622 39.42443 47.81381 89.4636 39.98196 39.89299 86.24314 39.98196 41.81278 82.88156 39.98196 43.47314 79.39972 39.98196 44.86377 75.81924 39.98196 45.97603 72.16233 39.98196 46.80302 68.45166 39.98196 47.33962 64.71028 39.98196 47.5825 60.96137 39.98196 47.53014 89.4102 40.6 39.81095 86.19635 40.6 41.72679 82.84169 40.6 43.38374 79.36702 40.6 44.7715 75.79391 40.6 45.88148 72.14451 40.6 46.70677 68.44149 40.6 47.24227 64.70778 40.6 47.48464 60.96659 40.6 47.43239 58.95371 11.5563 48.87318 62.81455 11.5563 49.07939 62.82207 11.35763 48.54095 66.67964 11.5563 48.98108 66.64476 11.35763 48.44372 70.525 11.5563 48.57886 70.44792 11.35763 48.04591 74.32677 11.5563 47.87523 74.208 11.35763 47.35 78.06137 11.5563 46.87454 81.70561 11.5563 45.58303 81.50588 11.35763 45.08294 85.2369 11.5563 44.00868 84.99842 11.35763 43.52587 88.63333 11.5563 42.16127 88.35758 11.35763 41.69873 91.87379 11.5563 40.05227 91.56251 11.35763 39.61286 59.00359 11.35763 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5783 13534 5787 13535 5789 13535 5790 13535 5791 1162 5793 1162 5796 1162 5794 1162 5796 1162 5793 1162 5799 1162 5800 1162 5797 1162 5783 13536 5785 13536 5787 13536 5787 1162 5791 1162 5797 1162 5797 1162 5781 1162 5787 1162 6279 13537 5141 13537 5140 13537 8561 1162 579 1162 8911 1162 8561 1162 8911 1162 8895 1162 8906 1049 829 1049 8534 1049

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13309 6606 13309 6606 13310 6615 13310 7565 13310 4455 4113 4456 4113 2995 4113 2675 268 2677 268 2754 268 2756 268 2753 268 2754 268 2756 13311 2754 13311 2677 13311 2673 13312 2675 13312 2754 13312 2674 268 2673 268 2754 268 2754 268 2752 268 2674 268 2750 268 2724 268 3168 268 2674 268 2752 268 2671 268 2724 13313 3164 13313 3168 13313 2750 13314 3168 13314 3165 13314 2752 13315 2672 13315 2671 13315 2752 13316 3154 13316 2672 13316 2750 13317 3165 13317 3161 13317 2750 13318 3161 13318 3160 13318 2752 268 3150 268 3154 268 2752 268 2751 268 3150 268 2750 13319 3160 13319 3171 13319 2750 13320 3171 13320 2751 13320 3150 268 2751 268 3177 268 3151 268 3150 268 3175 268 3171 13321 3173 13321 2751 13321 2751 13322 3173 13322 3172 13322 3147 13323 3151 13323 3153 13323 2751 268 3172 268 3178 268 3148 13324 3147 13324 3149 13324 2751 13325 3178 13325 3177 13325 3149 268 3152 268 3156 268 3156 13326 3148 13326 3149 13326 3150 268 3177 268 3175 268 3153 268 3149 268 3147 268 3159 13327 3170 13327 3167 13327 3179 268 3167 268 3170 268 3176 268 3179 268 3170 268 3170 13328 3169 13328 3176 13328 3151 268 3175 268 3153 268 3175 268 3174 268 3153 268 3170 268 3174 268 3169 268 7209 13329 7146 13329 7200 13329 6305 13330 6304 13330 4782 13330 4793 13331 5662 13331 5661 13331 4782 13332 6304 13332 4793 13332 4830 13333 6285 13333 4824 13333 6284 13334 4824 13334 6285 13334 5638 13335 5637 13335 4830 13335 6322 8624 6321 8624 5158 8624 7228 13336 7226 13336 1006 13336 1005 13337 1008 13337 1007 13337 1007 13338 1006 13338 1005 13338 7275 13339 6294 13339 6199 13339 4818 13340 6199 13340 6294 13340 7182 13341 1240 13341 7275 13341 7297 13342 6316 13342 6207 13342 4710 13343 6207 13343 6316 13343 7176 13344 1318 13344 7297 13344 8240 13345 8007 13345 8008 13345 8006 13346 8239 13346 8241 13346 8197 13347 8137 13347 8136 13347 8335 13348 8045 13348 8046 13348 8096 13349 8200 13349 8201 13349 8098 13350 8201 13350 8202 13350 8016 13351 8014 13351 8248 13351 8090 13352 8091 13352 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8001 13383 8309 13383 8311 13383 4604 268 4603 268 4607 268 4694 268 4688 268 4603 268 4690 268 4689 268 4607 268 4691 268 4685 268 4689 268 4684 13384 4692 13384 4687 13384 4687 268 4686 268 4684 268 4693 268 4600 268 4686 268 4606 268 4605 268 4600 268 4602 268 4601 268 4608 268 4608 268 4607 268 4602 268 4603 13385 4688 13385 4607 13385 4689 268 4685 268 4684 268 4684 13386 4686 13386 4600 13386 4600 13387 4605 13387 4602 13387 4602 13388 4607 13388 4600 13388 4688 268 4690 268 4607 268 4684 268 4600 268 4689 268 7996 13389 8313 13389 8315 13389 8184 13390 8133 13390 8132 13390 8366 13391 8043 13391 8044 13391 8368 13392 8048 13392 8053 13392 7995 13393 8326 13393 8325 13393 8282 13394 7984 13394 7985 13394 8060 13395 8329 13395 8331 13395 8155 13396 8105 13396 8103 13396 8047 13397 8336 13397 8337 13397 8069 13398 8068 13398 8339 13398 8162 13399 8127 13399 8126 13399 8164 13400 8128 13400 8129 13400 8166 13401 8121 13401 8120 13401 8169 13402 8124 13402 8121 13402 8170 13403 8111 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7995 8420 7995 8455 7995 2289 7995 8445 7995 8444 7995 2278 7995 8446 7995 8445 7995 2279 7995 8455 7995 8454 7995 2288 7995 8439 7995 8438 7995 2272 7995 8451 7995 8450 7995 2284 7995 8450 7995 8449 7995 2283 7995 8426 7995 8425 7995 2259 7995 8434 7995 8433 7995 2267 7995 8440 7995 8439 7995 2273 7995 8425 7995 8424 7995 2258 7995 8444 7995 8443 7995 2277 7995 8436 7995 8435 7995 2269 7995 8427 7995 8426 7995 2260 7995 8430 7995 8429 7995 2263 7995 8437 7995 8436 7995 2270 7995 8421 7995 8420 7995 2254 7995 5788 1165 5787 1165 5789 1165 5786 13492 5785 13492 5787 13492 5784 13493 5783 13493 5785 13493 5782 1165 5781 1165 5783 1165 5800 1165 5799 1165 5797 1165 5798 1165 5797 1165 5799 1165 5796 1165 5795 1165 5794 1165 5794 1165 5793 1165 5796 1165 5792 1165 5791 1165 5793 1165 5790 1165 5789 1165 5787 1165 5787 13494 5785 13494 5783 13494 5783 13495 5781 13495 5787 13495 5800 1165 5797 1165 5781 1165 5796 1165 5793 1165 5791 1165 5791 1165 5790 1165 5787 1165 5787 13496 5781 13496 5797 13496 5797 13497 5791 13497 5787 13497 5140 13498 5141 13498 6279 13498 8561 13499 576 13499 8895 13499 8561 1165 8895 1165 8911 1165 8534 7995 829 7995 8906 7995

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Redistributions of source code must retain the above copyright - notice, this list of conditions and the following disclaimer. - -2. Redistributions in binary form must reproduce the above copyright - notice, this list of conditions and the following disclaimer in - the documentation and/or other materials provided with the - distribution. - -3. Neither the name of the copyright holder nor the names of its - contributors may be used to endorse or promote products derived - from this software without specific prior written permission. - -THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS -"AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT -LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR -A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT -HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, -SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT -LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, -DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY -THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT -(INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE -OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. diff --git a/resources/robots/anymal_c/meshes/base.dae b/resources/robots/anymal_c/meshes/base.dae deleted file mode 100644 index c07f525d..00000000 --- a/resources/robots/anymal_c/meshes/base.dae +++ /dev/null @@ -1,326 +0,0 @@ - - - - - Blender User - Blender 2.81.16 commit date:2019-11-20, commit time:14:27, hash:26bd5ebd42e3 - - 2020-01-07T14:05:52 - 2020-01-07T14:05:52 - - Z_UP - - - - - - - base - - - - - base-surface - - - - - - 0 0 0 1 - - - - - - 1.45 - - - - - - - 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256 2006 255 2006 390 2007 325 2007 324 2007 146 2008 148 2008 276 2008 1184 1 1274 1 1273 1 164 2009 162 2009 161 2009 1186 1 1276 1 1275 1 623 2010 558 2010 557 2010 577 2011 576 2011 641 2011 139 15 723 15 722 15 254 2012 192 2012 194 2012 1214 1 1238 1 1237 1 109 2013 695 2013 694 2013 281 2014 138 2014 140 2014 306 2015 218 2015 220 2015 422 2016 421 2016 486 2016 406 2017 405 2017 470 2017 99 2018 685 2018 684 2018 179 599 706 599 705 599 593 2019 592 2019 657 2019 76 2020 662 2020 661 2020 551 2021 550 2021 615 2021 263 2022 174 2022 176 2022 811 15 812 15 754 15 1189 2023 1190 2023 1248 2023 114 2024 115 2024 116 2024 1183 1 1273 1 1272 1 185 15 704 15 716 15 293 2025 244 2025 116 2025 1210 2026 1211 2026 1235 2026 248 2027 247 2027 312 2027 48 2028 636 2028 635 2028

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104 2356 86 105 2030 87 106 2357 88 107 74 89 108 73 90 109 75 91 110 74 89 111 75 91 112 76 92 113 239 93 114 167 94 115 168 95 116 5044 96 117 4992 97 118 5004 98 119 310 99 120 171 100 121 254 101 122 84 102 123 78 103 124 77 104 125 84 102 126 77 104 127 85 105 128 5189 106 129 5161 107 130 5185 108 131 235 109 132 245 110 133 244 111 134 4305 112 135 4300 113 136 4304 114 137 774 115 138 736 116 139 737 117 140 396 118 141 392 85 142 390 119 143 6 21 144 8 23 145 65 82 146 351 120 147 415 121 148 423 122 149 75 91 150 88 123 151 76 92 152 77 104 153 90 124 154 85 105 155 93 125 156 92 126 157 54 127 158 54 127 159 92 126 160 62 128 161 54 127 162 62 128 163 55 129 164 55 129 165 62 128 166 61 130 167 1237 131 168 2027 132 169 2029 133 170 4 134 171 3 135 172 96 136 173 267 137 174 163 138 175 265 139 176 98 140 177 99 141 178 100 142 179 100 142 180 99 141 181 101 143 182 100 142 183 101 143 184 102 144 185 103 145 186 99 141 187 104 146 188 104 146 189 99 141 190 98 140 191 274 147 192 275 148 193 279 149 194 3929 150 195 3930 151 196 3928 152 197 272 153 198 276 154 199 270 155 200 455 156 201 369 157 202 371 158 203 109 159 204 37 160 205 110 161 206 110 161 207 37 160 208 111 162 209 110 161 210 111 162 211 112 163 212 112 163 213 111 162 214 113 164 215 112 163 216 113 164 217 32 39 218 283 165 219 179 166 220 282 167 221 587 168 222 833 169 223 588 170 224 5 171 225 11 56 226 47 58 227 425 172 228 440 173 229 441 174 230 5375 175 231 5378 176 232 5322 177 233 5382 178 234 5379 179 235 5381 180 236 5380 181 237 5324 182 238 5323 183 239 340 184 240 460 185 241 341 186 242 293 187 243 291 188 244 292 189 245 292 189 246 294 190 247 293 187 248 273 191 249 293 187 250 294 190 251 294 190 252 295 192 253 273 191 254 103 145 255 126 193 256 99 141 257 295 192 258 296 194 259 269 195 260 99 141 261 127 196 262 101 143 263 104 146 264 128 197 265 103 145 266 5313 198 267 5337 199 268 5350 200 269 124 201 270 129 202 271 130 203 272 122 204 273 130 203 274 18 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257 360 5373 258 361 5374 259 362 5354 260 363 5362 261 364 5361 262 365 94 263 366 141 264 367 83 265 368 290 266 369 311 267 370 322 268 371 142 269 372 154 270 373 143 271 374 4028 272 375 4030 273 376 4021 274 377 155 275 378 137 276 379 140 277 380 144 278 381 156 279 382 157 280 383 144 278 384 157 280 385 81 281 386 81 281 387 157 280 388 158 282 389 80 283 390 159 284 391 72 285 392 5315 286 393 5403 287 394 5317 288 395 439 289 396 443 290 397 444 291 398 5385 292 399 5377 253 400 5376 293 401 5386 294 402 5383 252 403 5385 292 404 451 295 405 373 296 406 375 250 407 1486 297 408 847 298 409 572 117 410 2954 299 411 2951 300 412 2953 301 413 5311 243 414 5361 262 415 5360 257 416 5346 302 417 5332 303 418 5345 304 419 169 305 420 170 306 421 171 100 422 172 307 423 173 308 424 174 309 425 11 56 426 9 65 427 10 57 428 178 310 429 179 166 430 180 311 431 181 312 432 182 313 433 183 314 434 183 314 435 182 313 436 184 315 437 189 316 438 190 317 439 191 318 440 196 319 441 195 320 442 189 316 443 196 319 444 189 316 445 197 321 446 197 321 447 189 316 448 191 318 449 192 322 450 198 323 451 199 324 452 199 324 453 200 17 454 201 325 455 204 220 456 203 326 457 205 327 458 204 220 459 205 327 460 200 17 461 200 17 462 205 327 463 206 328 464 200 17 465 206 328 466 201 325 467 179 166 468 178 310 469 207 329 470 246 330 471 244 111 472 247 331 473 180 311 474 209 332 475 210 333 476 180 311 477 210 333 478 178 310 479 3056 334 480 1571 335 481 1902 336 482 44 54 483 43 337 484 42 338 485 217 339 486 216 340 487 218 341 488 177 342 489 176 343 490 219 344 491 48 60 492 47 58 493 10 57 494 221 345 495 220 346 496 222 347 497 50 61 498 39 348 499 49 59 500 175 349 501 224 350 502 225 351 503 55 129 504 52 66 505 54 127 506 173 308 507 228 73 508 174 309 509 62 128 510 59 352 511 61 130 512 235 109 513 236 353 514 172 307 515 389 354 516 410 355 517 390 119 518 5339 356 519 5345 304 520 5338 357 521 168 95 522 241 358 523 242 359 524 2912 360 525 2949 361 526 2948 362 527 236 353 528 235 109 529 246 330 530 617 363 531 5333 364 532 5334 365 533 248 366 534 243 367 535 249 368 536 171 100 537 170 306 538 240 369 539 240 369 540 170 306 541 250 370 542 240 369 543 250 370 544 241 358 545 241 358 546 250 370 547 251 371 548 241 358 549 251 371 550 242 359 551 252 372 552 169 305 553 253 373 554 253 373 555 169 305 556 171 100 557 387 374 558 405 375 559 407 376 560 254 101 561 171 100 562 240 369 563 409 377 564 411 378 565 404 379 566 256 380 567 167 94 568 166 381 569 256 380 570 166 381 571 257 245 572 415 121 573 416 382 574 422 383 575 298 384 576 297 385 577 301 216 578 623 386 579 5334 365 580 5313 198 581 5332 303 582 628 387 583 627 388 584 77 104 585 66 389 586 90 124 587 5337 199 588 5348 390 589 5351 391 590 93 125 591 86 392 592 92 126 593 172 307 594 174 309 595 235 109 596 5358 393 597 5357 394 598 5320 395 599 5349 396 600 5320 395 601 5357 394 602 242 359 603 237 397 604 168 95 605 166 381 606 260 398 607 257 245 608 263 399 609 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