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cheetah_env.py
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cheetah_env.py
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import numpy as np
from gym import utils
from gym.envs.mujoco import mujoco_env
class HalfCheetahEnvNew(mujoco_env.MujocoEnv, utils.EzPickle):
def __init__(self):
mujoco_env.MujocoEnv.__init__(self, 'half_cheetah.xml', 1)
utils.EzPickle.__init__(self)
def _step(self, action):
xposbefore = self.model.data.qpos[0, 0]
self.do_simulation(action, self.frame_skip)
xposafter = self.model.data.qpos[0, 0]
ob = self._get_obs()
reward_ctrl = - 0.1 * np.square(action).sum()
reward_run = (xposafter - xposbefore)/self.dt
reward = reward_ctrl + reward_run
done = False
return ob, reward, done, dict(reward_run=reward_run, reward_ctrl=reward_ctrl)
def _get_obs(self):
return np.concatenate([
self.model.data.qpos.flat[1:],
self.model.data.qvel.flat,
self.get_body_com("torso").flat,
# self.get_body_comvel("torso").flat,
])
def reset_model(self):
qpos = self.init_qpos + self.np_random.uniform(low=-.1, high=.1, size=self.model.nq)
qvel = self.init_qvel + self.np_random.randn(self.model.nv) * .1
self.set_state(qpos, qvel)
return self._get_obs()
def viewer_setup(self):
self.viewer.cam.distance = self.model.stat.extent * 0.5