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| import numpy as np | |
| from gym import utils | |
| from gym.envs.mujoco import MuJocoPyEnv | |
| from gym.spaces import Box | |
| class InvertedPendulumEnv(MuJocoPyEnv, utils.EzPickle): | |
| metadata = { | |
| "render_modes": [ | |
| "human", | |
| "rgb_array", | |
| "depth_array", | |
| ], | |
| "render_fps": 25, | |
| } | |
| def __init__(self, **kwargs): | |
| utils.EzPickle.__init__(self, **kwargs) | |
| observation_space = Box(low=-np.inf, high=np.inf, shape=(4,), dtype=np.float64) | |
| MuJocoPyEnv.__init__( | |
| self, | |
| "inverted_pendulum.xml", | |
| 2, | |
| observation_space=observation_space, | |
| **kwargs | |
| ) | |
| def step(self, a): | |
| reward = 1.0 | |
| self.do_simulation(a, self.frame_skip) | |
| ob = self._get_obs() | |
| terminated = bool(not np.isfinite(ob).all() or (np.abs(ob[1]) > 0.2)) | |
| if self.render_mode == "human": | |
| self.render() | |
| return ob, reward, terminated, False, {} | |
| def reset_model(self): | |
| qpos = self.init_qpos + self.np_random.uniform( | |
| size=self.model.nq, low=-0.01, high=0.01 | |
| ) | |
| qvel = self.init_qvel + self.np_random.uniform( | |
| size=self.model.nv, low=-0.01, high=0.01 | |
| ) | |
| self.set_state(qpos, qvel) | |
| return self._get_obs() | |
| def _get_obs(self): | |
| return np.concatenate([self.sim.data.qpos, self.sim.data.qvel]).ravel() | |
| def viewer_setup(self): | |
| assert self.viewer is not None | |
| self.viewer.cam.trackbodyid = 0 | |
| self.viewer.cam.distance = self.model.stat.extent | |