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| import numpy as np | |
| from gym import utils | |
| from gym.envs.mujoco import MuJocoPyEnv | |
| from gym.spaces import Box | |
| class ReacherEnv(MuJocoPyEnv, utils.EzPickle): | |
| metadata = { | |
| "render_modes": [ | |
| "human", | |
| "rgb_array", | |
| "depth_array", | |
| ], | |
| "render_fps": 50, | |
| } | |
| def __init__(self, **kwargs): | |
| utils.EzPickle.__init__(self, **kwargs) | |
| observation_space = Box(low=-np.inf, high=np.inf, shape=(11,), dtype=np.float64) | |
| MuJocoPyEnv.__init__( | |
| self, "reacher.xml", 2, observation_space=observation_space, **kwargs | |
| ) | |
| def step(self, a): | |
| vec = self.get_body_com("fingertip") - self.get_body_com("target") | |
| reward_dist = -np.linalg.norm(vec) | |
| reward_ctrl = -np.square(a).sum() | |
| reward = reward_dist + reward_ctrl | |
| self.do_simulation(a, self.frame_skip) | |
| if self.render_mode == "human": | |
| self.render() | |
| ob = self._get_obs() | |
| return ( | |
| ob, | |
| reward, | |
| False, | |
| False, | |
| dict(reward_dist=reward_dist, reward_ctrl=reward_ctrl), | |
| ) | |
| def viewer_setup(self): | |
| assert self.viewer is not None | |
| self.viewer.cam.trackbodyid = 0 | |
| def reset_model(self): | |
| qpos = ( | |
| self.np_random.uniform(low=-0.1, high=0.1, size=self.model.nq) | |
| + self.init_qpos | |
| ) | |
| while True: | |
| self.goal = self.np_random.uniform(low=-0.2, high=0.2, size=2) | |
| if np.linalg.norm(self.goal) < 0.2: | |
| break | |
| qpos[-2:] = self.goal | |
| qvel = self.init_qvel + self.np_random.uniform( | |
| low=-0.005, high=0.005, size=self.model.nv | |
| ) | |
| qvel[-2:] = 0 | |
| self.set_state(qpos, qvel) | |
| return self._get_obs() | |
| def _get_obs(self): | |
| theta = self.sim.data.qpos.flat[:2] | |
| return np.concatenate( | |
| [ | |
| np.cos(theta), | |
| np.sin(theta), | |
| self.sim.data.qpos.flat[2:], | |
| self.sim.data.qvel.flat[:2], | |
| self.get_body_com("fingertip") - self.get_body_com("target"), | |
| ] | |
| ) | |