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31 lines
1.0 KiB
Python
31 lines
1.0 KiB
Python
import numpy as np
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from gym import utils
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from gym.envs.mujoco import mujoco_env
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class InvertedPendulumEnv(mujoco_env.MujocoEnv, utils.EzPickle):
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def __init__(self):
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utils.EzPickle.__init__(self)
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mujoco_env.MujocoEnv.__init__(self, 'inverted_pendulum.xml', 2)
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def _step(self, a):
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reward = 1.0
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self.do_simulation(a, self.frame_skip)
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ob = self._get_obs()
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notdone = np.isfinite(ob).all() and (np.abs(ob[1]) <= .2)
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done = not notdone
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return ob, reward, done, {}
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def reset_model(self):
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qpos = self.init_qpos + self.np_random.uniform(size=self.model.nq, low=-0.01, high=0.01)
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qvel = self.init_qvel + self.np_random.uniform(size=self.model.nv, low=-0.01, high=0.01)
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self.set_state(qpos, qvel)
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return self._get_obs()
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def _get_obs(self):
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return np.concatenate([self.model.data.qpos, self.model.data.qvel]).ravel()
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def viewer_setup(self):
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v = self.viewer
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v.cam.trackbodyid = 0
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v.cam.distance = v.model.stat.extent
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