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Gymnasium/gym/envs/mujoco/inverted_double_pendulum.py
2022-05-24 08:47:51 -04:00

50 lines
1.6 KiB
Python

import numpy as np
from gym import utils
from gym.envs.mujoco import mujoco_env
class InvertedDoublePendulumEnv(mujoco_env.MujocoEnv, utils.EzPickle):
def __init__(self):
mujoco_env.MujocoEnv.__init__(
self, "inverted_double_pendulum.xml", 5, mujoco_bindings="mujoco_py"
)
utils.EzPickle.__init__(self)
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
done = bool(y <= 1)
return ob, r, done, {}
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):
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]