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import numpy as np
from gym import utils
from gym.envs.mujoco import MuJocoPyEnv
from gym.spaces import Box
class AntEnv(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=(111,), dtype=np.float64
)
MuJocoPyEnv.__init__(
self, "ant.xml", 5, observation_space=observation_space, **kwargs
)
utils.EzPickle.__init__(self, **kwargs)
def step(self, a):
xposbefore = self.get_body_com("torso")[0]
self.do_simulation(a, self.frame_skip)
xposafter = self.get_body_com("torso")[0]
forward_reward = (xposafter - xposbefore) / self.dt
ctrl_cost = 0.5 * np.square(a).sum()
contact_cost = (
0.5 * 1e-3 * np.sum(np.square(np.clip(self.sim.data.cfrc_ext, -1, 1)))
)
survive_reward = 1.0
reward = forward_reward - ctrl_cost - contact_cost + survive_reward
state = self.state_vector()
not_terminated = (
np.isfinite(state).all() and state[2] >= 0.2 and state[2] <= 1.0
)
terminated = not not_terminated
ob = self._get_obs()
if self.render_mode == "human":
self.render()
return (
ob,
reward,
terminated,
False,
dict(
reward_forward=forward_reward,
reward_ctrl=-ctrl_cost,
reward_contact=-contact_cost,
reward_survive=survive_reward,
),
)
def _get_obs(self):
return np.concatenate(
[
self.sim.data.qpos.flat[2:],
self.sim.data.qvel.flat,
np.clip(self.sim.data.cfrc_ext, -1, 1).flat,
]
)
def reset_model(self):
qpos = self.init_qpos + self.np_random.uniform(
size=self.model.nq, low=-0.1, high=0.1
)
qvel = self.init_qvel + self.np_random.standard_normal(self.model.nv) * 0.1
self.set_state(qpos, qvel)
return self._get_obs()
def viewer_setup(self):
assert self.viewer is not None
self.viewer.cam.distance = self.model.stat.extent * 0.5
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