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| import numpy as np | |
| from gym import utils | |
| from gym.envs.mujoco import MuJocoPyEnv | |
| from gym.spaces import Box | |
| class SwimmerEnv(MuJocoPyEnv, utils.EzPickle): | |
| metadata = { | |
| "render_modes": [ | |
| "human", | |
| "rgb_array", | |
| "depth_array", | |
| ], | |
| "render_fps": 25, | |
| } | |
| def __init__(self, **kwargs): | |
| observation_space = Box(low=-np.inf, high=np.inf, shape=(8,), dtype=np.float64) | |
| MuJocoPyEnv.__init__( | |
| self, "swimmer.xml", 4, observation_space=observation_space, **kwargs | |
| ) | |
| utils.EzPickle.__init__(self, **kwargs) | |
| def step(self, a): | |
| ctrl_cost_coeff = 0.0001 | |
| xposbefore = self.sim.data.qpos[0] | |
| self.do_simulation(a, self.frame_skip) | |
| xposafter = self.sim.data.qpos[0] | |
| reward_fwd = (xposafter - xposbefore) / self.dt | |
| reward_ctrl = -ctrl_cost_coeff * np.square(a).sum() | |
| reward = reward_fwd + reward_ctrl | |
| ob = self._get_obs() | |
| if self.render_mode == "human": | |
| self.render() | |
| return ( | |
| ob, | |
| reward, | |
| False, | |
| False, | |
| dict(reward_fwd=reward_fwd, reward_ctrl=reward_ctrl), | |
| ) | |
| def _get_obs(self): | |
| qpos = self.sim.data.qpos | |
| qvel = self.sim.data.qvel | |
| return np.concatenate([qpos.flat[2:], qvel.flat]) | |
| def reset_model(self): | |
| self.set_state( | |
| self.init_qpos | |
| + self.np_random.uniform(low=-0.1, high=0.1, size=self.model.nq), | |
| self.init_qvel | |
| + self.np_random.uniform(low=-0.1, high=0.1, size=self.model.nv), | |
| ) | |
| return self._get_obs() | |