81 lines
2.4 KiB
Python
81 lines
2.4 KiB
Python
import numpy as np
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from gymnasium import utils
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from gymnasium.envs.mujoco import MuJocoPyEnv
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from gymnasium.spaces import Box
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class AntEnv(MuJocoPyEnv, 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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],
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"render_fps": 20,
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}
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def __init__(self, **kwargs):
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observation_space = Box(
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low=-np.inf, high=np.inf, shape=(111,), dtype=np.float64
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)
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MuJocoPyEnv.__init__(
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self, "ant.xml", 5, observation_space=observation_space, **kwargs
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)
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utils.EzPickle.__init__(self, **kwargs)
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def step(self, a):
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xposbefore = self.get_body_com("torso")[0]
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self.do_simulation(a, self.frame_skip)
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xposafter = self.get_body_com("torso")[0]
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forward_reward = (xposafter - xposbefore) / self.dt
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ctrl_cost = 0.5 * np.square(a).sum()
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contact_cost = (
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0.5 * 1e-3 * np.sum(np.square(np.clip(self.sim.data.cfrc_ext, -1, 1)))
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)
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survive_reward = 1.0
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reward = forward_reward - ctrl_cost - contact_cost + survive_reward
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state = self.state_vector()
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not_terminated = (
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np.isfinite(state).all() and state[2] >= 0.2 and state[2] <= 1.0
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)
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terminated = not not_terminated
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ob = self._get_obs()
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if self.render_mode == "human":
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self.render()
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return (
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ob,
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reward,
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terminated,
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False,
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dict(
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reward_forward=forward_reward,
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reward_ctrl=-ctrl_cost,
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reward_contact=-contact_cost,
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reward_survive=survive_reward,
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),
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)
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def _get_obs(self):
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return np.concatenate(
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[
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self.sim.data.qpos.flat[2:],
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self.sim.data.qvel.flat,
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np.clip(self.sim.data.cfrc_ext, -1, 1).flat,
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]
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)
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def reset_model(self):
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qpos = self.init_qpos + self.np_random.uniform(
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size=self.model.nq, low=-0.1, high=0.1
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)
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qvel = self.init_qvel + self.np_random.standard_normal(self.model.nv) * 0.1
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self.set_state(qpos, qvel)
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return self._get_obs()
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def viewer_setup(self):
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assert self.viewer is not None
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self.viewer.cam.distance = self.model.stat.extent * 0.5
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