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import numpy as np
from gym import utils
from gym.envs.mujoco import MuJocoPyEnv
from gym.spaces import Box
class HalfCheetahEnv(MuJocoPyEnv, utils.EzPickle):
metadata = {
"render_modes": [
"human",
"rgb_array",
"depth_array",
],
"render_fps": 20,
}
def __init__(self, **kwargs):
observation_space = Box(low=-np.inf, high=np.inf, shape=(17,), dtype=np.float64)
MuJocoPyEnv.__init__(
self, "half_cheetah.xml", 5, observation_space=observation_space, **kwargs
)
utils.EzPickle.__init__(self, **kwargs)
def step(self, action):
xposbefore = self.sim.data.qpos[0]
self.do_simulation(action, self.frame_skip)
xposafter = self.sim.data.qpos[0]
ob = self._get_obs()
reward_ctrl = -0.1 * np.square(action).sum()
reward_run = (xposafter - xposbefore) / self.dt
reward = reward_ctrl + reward_run
terminated = False
if self.render_mode == "human":
self.render()
return (
ob,
reward,
terminated,
False,
dict(reward_run=reward_run, reward_ctrl=reward_ctrl),
)
def _get_obs(self):
return np.concatenate(
[
self.sim.data.qpos.flat[1:],
self.sim.data.qvel.flat,
]
)
def reset_model(self):
qpos = self.init_qpos + self.np_random.uniform(
low=-0.1, high=0.1, size=self.model.nq
)
qvel = self.init_qvel + self.np_random.standard_normal(self.model.nv) * 0.1
self.set_state(qpos, qvel)
return self._get_obs()
def viewer_setup(self):
assert self.viewer is not None
self.viewer.cam.distance = self.model.stat.extent * 0.5
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