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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
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