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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
76 lines
2.2 KiB
Python
76 lines
2.2 KiB
Python
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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class ReacherEnv(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": 50,
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}
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def __init__(self, **kwargs):
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utils.EzPickle.__init__(self)
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observation_space = Box(low=-np.inf, high=np.inf, shape=(11,), dtype=np.float64)
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mujoco_env.MujocoEnv.__init__(
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self,
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"reacher.xml",
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2,
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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 step(self, a):
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vec = self.get_body_com("fingertip") - self.get_body_com("target")
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reward_dist = -np.linalg.norm(vec)
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reward_ctrl = -np.square(a).sum()
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reward = reward_dist + reward_ctrl
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self.do_simulation(a, self.frame_skip)
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self.renderer.render_step()
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ob = self._get_obs()
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done = False
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return ob, reward, done, dict(reward_dist=reward_dist, reward_ctrl=reward_ctrl)
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def viewer_setup(self):
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self.viewer.cam.trackbodyid = 0
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def reset_model(self):
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qpos = (
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self.np_random.uniform(low=-0.1, high=0.1, size=self.model.nq)
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+ self.init_qpos
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)
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while True:
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self.goal = self.np_random.uniform(low=-0.2, high=0.2, size=2)
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if np.linalg.norm(self.goal) < 0.2:
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break
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qpos[-2:] = self.goal
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qvel = self.init_qvel + self.np_random.uniform(
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low=-0.005, high=0.005, size=self.model.nv
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)
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qvel[-2:] = 0
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self.set_state(qpos, qvel)
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return self._get_obs()
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def _get_obs(self):
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theta = self.sim.data.qpos.flat[:2]
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return np.concatenate(
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[
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np.cos(theta),
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np.sin(theta),
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self.sim.data.qpos.flat[2:],
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self.sim.data.qvel.flat[:2],
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self.get_body_com("fingertip") - self.get_body_com("target"),
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]
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)
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