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| import numpy as np | |
| from gym import utils | |
| from gym.envs.mujoco import MuJocoPyEnv | |
| from gym.spaces import Box | |
| DEFAULT_CAMERA_CONFIG = { | |
| "trackbodyid": 1, | |
| "distance": 4.0, | |
| "lookat": np.array((0.0, 0.0, 2.0)), | |
| "elevation": -20.0, | |
| } | |
| def mass_center(model, sim): | |
| mass = np.expand_dims(model.body_mass, axis=1) | |
| xpos = sim.data.xipos | |
| return (np.sum(mass * xpos, axis=0) / np.sum(mass))[0:2].copy() | |
| class HumanoidEnv(MuJocoPyEnv, utils.EzPickle): | |
| metadata = { | |
| "render_modes": [ | |
| "human", | |
| "rgb_array", | |
| "depth_array", | |
| ], | |
| "render_fps": 67, | |
| } | |
| def __init__( | |
| self, | |
| xml_file="humanoid.xml", | |
| forward_reward_weight=1.25, | |
| ctrl_cost_weight=0.1, | |
| contact_cost_weight=5e-7, | |
| contact_cost_range=(-np.inf, 10.0), | |
| healthy_reward=5.0, | |
| terminate_when_unhealthy=True, | |
| healthy_z_range=(1.0, 2.0), | |
| reset_noise_scale=1e-2, | |
| exclude_current_positions_from_observation=True, | |
| **kwargs | |
| ): | |
| utils.EzPickle.__init__( | |
| self, | |
| xml_file, | |
| forward_reward_weight, | |
| ctrl_cost_weight, | |
| contact_cost_weight, | |
| contact_cost_range, | |
| healthy_reward, | |
| terminate_when_unhealthy, | |
| healthy_z_range, | |
| reset_noise_scale, | |
| exclude_current_positions_from_observation, | |
| **kwargs | |
| ) | |
| self._forward_reward_weight = forward_reward_weight | |
| self._ctrl_cost_weight = ctrl_cost_weight | |
| self._contact_cost_weight = contact_cost_weight | |
| self._contact_cost_range = contact_cost_range | |
| self._healthy_reward = healthy_reward | |
| self._terminate_when_unhealthy = terminate_when_unhealthy | |
| self._healthy_z_range = healthy_z_range | |
| self._reset_noise_scale = reset_noise_scale | |
| self._exclude_current_positions_from_observation = ( | |
| exclude_current_positions_from_observation | |
| ) | |
| if exclude_current_positions_from_observation: | |
| observation_space = Box( | |
| low=-np.inf, high=np.inf, shape=(376,), dtype=np.float64 | |
| ) | |
| else: | |
| observation_space = Box( | |
| low=-np.inf, high=np.inf, shape=(378,), dtype=np.float64 | |
| ) | |
| MuJocoPyEnv.__init__( | |
| self, xml_file, 5, observation_space=observation_space, **kwargs | |
| ) | |
| def healthy_reward(self): | |
| return ( | |
| float(self.is_healthy or self._terminate_when_unhealthy) | |
| * self._healthy_reward | |
| ) | |
| def control_cost(self, action): | |
| control_cost = self._ctrl_cost_weight * np.sum(np.square(self.sim.data.ctrl)) | |
| return control_cost | |
| def contact_cost(self): | |
| contact_forces = self.sim.data.cfrc_ext | |
| contact_cost = self._contact_cost_weight * np.sum(np.square(contact_forces)) | |
| min_cost, max_cost = self._contact_cost_range | |
| contact_cost = np.clip(contact_cost, min_cost, max_cost) | |
| return contact_cost | |
| def is_healthy(self): | |
| min_z, max_z = self._healthy_z_range | |
| is_healthy = min_z < self.sim.data.qpos[2] < max_z | |
| return is_healthy | |
| def terminated(self): | |
| terminated = (not self.is_healthy) if self._terminate_when_unhealthy else False | |
| return terminated | |
| def _get_obs(self): | |
| position = self.sim.data.qpos.flat.copy() | |
| velocity = self.sim.data.qvel.flat.copy() | |
| com_inertia = self.sim.data.cinert.flat.copy() | |
| com_velocity = self.sim.data.cvel.flat.copy() | |
| actuator_forces = self.sim.data.qfrc_actuator.flat.copy() | |
| external_contact_forces = self.sim.data.cfrc_ext.flat.copy() | |
| if self._exclude_current_positions_from_observation: | |
| position = position[2:] | |
| return np.concatenate( | |
| ( | |
| position, | |
| velocity, | |
| com_inertia, | |
| com_velocity, | |
| actuator_forces, | |
| external_contact_forces, | |
| ) | |
| ) | |
| def step(self, action): | |
| xy_position_before = mass_center(self.model, self.sim) | |
| self.do_simulation(action, self.frame_skip) | |
| xy_position_after = mass_center(self.model, self.sim) | |
| xy_velocity = (xy_position_after - xy_position_before) / self.dt | |
| x_velocity, y_velocity = xy_velocity | |
| ctrl_cost = self.control_cost(action) | |
| contact_cost = self.contact_cost | |
| forward_reward = self._forward_reward_weight * x_velocity | |
| healthy_reward = self.healthy_reward | |
| rewards = forward_reward + healthy_reward | |
| costs = ctrl_cost + contact_cost | |
| observation = self._get_obs() | |
| reward = rewards - costs | |
| terminated = self.terminated | |
| info = { | |
| "reward_linvel": forward_reward, | |
| "reward_quadctrl": -ctrl_cost, | |
| "reward_alive": healthy_reward, | |
| "reward_impact": -contact_cost, | |
| "x_position": xy_position_after[0], | |
| "y_position": xy_position_after[1], | |
| "distance_from_origin": np.linalg.norm(xy_position_after, ord=2), | |
| "x_velocity": x_velocity, | |
| "y_velocity": y_velocity, | |
| "forward_reward": forward_reward, | |
| } | |
| if self.render_mode == "human": | |
| self.render() | |
| return observation, reward, terminated, False, info | |
| def reset_model(self): | |
| noise_low = -self._reset_noise_scale | |
| noise_high = self._reset_noise_scale | |
| qpos = self.init_qpos + self.np_random.uniform( | |
| low=noise_low, high=noise_high, size=self.model.nq | |
| ) | |
| qvel = self.init_qvel + self.np_random.uniform( | |
| low=noise_low, high=noise_high, size=self.model.nv | |
| ) | |
| self.set_state(qpos, qvel) | |
| observation = self._get_obs() | |
| return observation | |
| def viewer_setup(self): | |
| assert self.viewer is not None | |
| for key, value in DEFAULT_CAMERA_CONFIG.items(): | |
| if isinstance(value, np.ndarray): | |
| getattr(self.viewer.cam, key)[:] = value | |
| else: | |
| setattr(self.viewer.cam, key, value) | |