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import numpy as np
from gym import utils
from gym.envs.mujoco import MuJocoPyEnv
from gym.spaces import Box
class InvertedDoublePendulumEnv(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=(11,), dtype=np.float64)
MuJocoPyEnv.__init__(
self,
"inverted_double_pendulum.xml",
5,
observation_space=observation_space,
**kwargs
)
utils.EzPickle.__init__(self, **kwargs)
def step(self, action):
self.do_simulation(action, self.frame_skip)
ob = self._get_obs()
x, _, y = self.sim.data.site_xpos[0]
dist_penalty = 0.01 * x**2 + (y - 2) ** 2
v1, v2 = self.sim.data.qvel[1:3]
vel_penalty = 1e-3 * v1**2 + 5e-3 * v2**2
alive_bonus = 10
r = alive_bonus - dist_penalty - vel_penalty
terminated = bool(y <= 1)
if self.render_mode == "human":
self.render()
return ob, r, terminated, False, {}
def _get_obs(self):
return np.concatenate(
[
self.sim.data.qpos[:1], # cart x pos
np.sin(self.sim.data.qpos[1:]), # link angles
np.cos(self.sim.data.qpos[1:]),
np.clip(self.sim.data.qvel, -10, 10),
np.clip(self.sim.data.qfrc_constraint, -10, 10),
]
).ravel()
def reset_model(self):
self.set_state(
self.init_qpos
+ self.np_random.uniform(low=-0.1, high=0.1, size=self.model.nq),
self.init_qvel + self.np_random.standard_normal(self.model.nv) * 0.1,
)
return self._get_obs()
def viewer_setup(self):
assert self.viewer is not None
v = self.viewer
v.cam.trackbodyid = 0
v.cam.distance = self.model.stat.extent * 0.5
v.cam.lookat[2] = 0.12250000000000005 # v.model.stat.center[2]
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