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43 lines
1.3 KiB
Python
43 lines
1.3 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 HalfCheetahEnv(mujoco_env.MujocoEnv, utils.EzPickle):
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def __init__(self):
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mujoco_env.MujocoEnv.__init__(
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self, "half_cheetah.xml", 5, mujoco_bindings="mujoco_py"
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)
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utils.EzPickle.__init__(self)
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def step(self, action):
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xposbefore = self.sim.data.qpos[0]
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self.do_simulation(action, self.frame_skip)
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xposafter = self.sim.data.qpos[0]
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ob = self._get_obs()
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reward_ctrl = -0.1 * np.square(action).sum()
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reward_run = (xposafter - xposbefore) / self.dt
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reward = reward_ctrl + reward_run
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done = False
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return ob, reward, done, dict(reward_run=reward_run, reward_ctrl=reward_ctrl)
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def _get_obs(self):
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return np.concatenate(
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[
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self.sim.data.qpos.flat[1:],
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self.sim.data.qvel.flat,
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]
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)
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def reset_model(self):
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qpos = self.init_qpos + self.np_random.uniform(
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low=-0.1, high=0.1, size=self.model.nq
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)
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qvel = self.init_qvel + self.np_random.standard_normal(self.model.nv) * 0.1
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self.set_state(qpos, qvel)
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return self._get_obs()
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def viewer_setup(self):
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self.viewer.cam.distance = self.model.stat.extent * 0.5
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