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61a39f41bc
* Remove step initialization for mujoco obs spaces * remove step initialization for mujoco obs space * pre-commit pytest obs space mujoco
127 lines
3.7 KiB
Python
127 lines
3.7 KiB
Python
__credits__ = ["Rushiv Arora"]
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import numpy as np
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from gym import utils
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from gym.envs.mujoco import mujoco_env
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from gym.spaces import Box
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DEFAULT_CAMERA_CONFIG = {}
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class SwimmerEnv(mujoco_env.MujocoEnv, utils.EzPickle):
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metadata = {
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"render_modes": [
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"human",
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"rgb_array",
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"depth_array",
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"single_rgb_array",
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"single_depth_array",
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],
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"render_fps": 25,
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}
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def __init__(
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self,
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xml_file="swimmer.xml",
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forward_reward_weight=1.0,
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ctrl_cost_weight=1e-4,
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reset_noise_scale=0.1,
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exclude_current_positions_from_observation=True,
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**kwargs
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):
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utils.EzPickle.__init__(**locals())
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self._forward_reward_weight = forward_reward_weight
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self._ctrl_cost_weight = ctrl_cost_weight
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self._reset_noise_scale = reset_noise_scale
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self._exclude_current_positions_from_observation = (
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exclude_current_positions_from_observation
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)
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if exclude_current_positions_from_observation:
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observation_space = Box(
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low=-np.inf, high=np.inf, shape=(8,), dtype=np.float64
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)
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else:
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observation_space = Box(
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low=-np.inf, high=np.inf, shape=(10,), dtype=np.float64
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)
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mujoco_env.MujocoEnv.__init__(
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self,
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xml_file,
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4,
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mujoco_bindings="mujoco_py",
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observation_space=observation_space,
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**kwargs
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)
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def control_cost(self, action):
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control_cost = self._ctrl_cost_weight * np.sum(np.square(action))
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return control_cost
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def step(self, action):
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xy_position_before = self.sim.data.qpos[0:2].copy()
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self.do_simulation(action, self.frame_skip)
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xy_position_after = self.sim.data.qpos[0:2].copy()
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self.renderer.render_step()
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xy_velocity = (xy_position_after - xy_position_before) / self.dt
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x_velocity, y_velocity = xy_velocity
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forward_reward = self._forward_reward_weight * x_velocity
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ctrl_cost = self.control_cost(action)
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observation = self._get_obs()
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reward = forward_reward - ctrl_cost
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done = False
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info = {
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"reward_fwd": forward_reward,
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"reward_ctrl": -ctrl_cost,
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"x_position": xy_position_after[0],
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"y_position": xy_position_after[1],
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"distance_from_origin": np.linalg.norm(xy_position_after, ord=2),
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"x_velocity": x_velocity,
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"y_velocity": y_velocity,
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"forward_reward": forward_reward,
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}
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return observation, reward, done, info
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def _get_obs(self):
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position = self.sim.data.qpos.flat.copy()
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velocity = self.sim.data.qvel.flat.copy()
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if self._exclude_current_positions_from_observation:
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position = position[2:]
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observation = np.concatenate([position, velocity]).ravel()
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return observation
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def reset_model(self):
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noise_low = -self._reset_noise_scale
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noise_high = self._reset_noise_scale
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qpos = self.init_qpos + self.np_random.uniform(
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low=noise_low, high=noise_high, size=self.model.nq
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)
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qvel = self.init_qvel + self.np_random.uniform(
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low=noise_low, high=noise_high, size=self.model.nv
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)
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self.set_state(qpos, qvel)
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observation = self._get_obs()
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return observation
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def viewer_setup(self):
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for key, value in DEFAULT_CAMERA_CONFIG.items():
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if isinstance(value, np.ndarray):
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getattr(self.viewer.cam, key)[:] = value
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else:
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setattr(self.viewer.cam, key, value)
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