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| __credits__ = ["Rushiv Arora"] | |
| import numpy as np | |
| from gym import utils | |
| from gym.envs.mujoco import MuJocoPyEnv | |
| from gym.spaces import Box | |
| DEFAULT_CAMERA_CONFIG = {} | |
| class SwimmerEnv(MuJocoPyEnv, utils.EzPickle): | |
| metadata = { | |
| "render_modes": [ | |
| "human", | |
| "rgb_array", | |
| "depth_array", | |
| ], | |
| "render_fps": 25, | |
| } | |
| def __init__( | |
| self, | |
| xml_file="swimmer.xml", | |
| forward_reward_weight=1.0, | |
| ctrl_cost_weight=1e-4, | |
| reset_noise_scale=0.1, | |
| exclude_current_positions_from_observation=True, | |
| **kwargs | |
| ): | |
| utils.EzPickle.__init__( | |
| self, | |
| xml_file, | |
| forward_reward_weight, | |
| ctrl_cost_weight, | |
| reset_noise_scale, | |
| exclude_current_positions_from_observation, | |
| **kwargs | |
| ) | |
| self._forward_reward_weight = forward_reward_weight | |
| self._ctrl_cost_weight = ctrl_cost_weight | |
| 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=(8,), dtype=np.float64 | |
| ) | |
| else: | |
| observation_space = Box( | |
| low=-np.inf, high=np.inf, shape=(10,), dtype=np.float64 | |
| ) | |
| MuJocoPyEnv.__init__( | |
| self, xml_file, 4, observation_space=observation_space, **kwargs | |
| ) | |
| def control_cost(self, action): | |
| control_cost = self._ctrl_cost_weight * np.sum(np.square(action)) | |
| return control_cost | |
| def step(self, action): | |
| xy_position_before = self.sim.data.qpos[0:2].copy() | |
| self.do_simulation(action, self.frame_skip) | |
| xy_position_after = self.sim.data.qpos[0:2].copy() | |
| xy_velocity = (xy_position_after - xy_position_before) / self.dt | |
| x_velocity, y_velocity = xy_velocity | |
| forward_reward = self._forward_reward_weight * x_velocity | |
| ctrl_cost = self.control_cost(action) | |
| observation = self._get_obs() | |
| reward = forward_reward - ctrl_cost | |
| info = { | |
| "reward_fwd": forward_reward, | |
| "reward_ctrl": -ctrl_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, False, False, info | |
| def _get_obs(self): | |
| position = self.sim.data.qpos.flat.copy() | |
| velocity = self.sim.data.qvel.flat.copy() | |
| if self._exclude_current_positions_from_observation: | |
| position = position[2:] | |
| observation = np.concatenate([position, velocity]).ravel() | |
| return observation | |
| 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) | |