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import numpy as np
from gym import utils
from gym.envs.mujoco import MuJocoPyEnv
from gym.spaces import Box
class HumanoidStandupEnv(MuJocoPyEnv, utils.EzPickle):
metadata = {
"render_modes": [
"human",
"rgb_array",
"depth_array",
],
"render_fps": 67,
}
def __init__(self, **kwargs):
observation_space = Box(
low=-np.inf, high=np.inf, shape=(376,), dtype=np.float64
)
MuJocoPyEnv.__init__(
self,
"humanoidstandup.xml",
5,
observation_space=observation_space,
**kwargs
)
utils.EzPickle.__init__(self, **kwargs)
def _get_obs(self):
data = self.sim.data
return np.concatenate(
[
data.qpos.flat[2:],
data.qvel.flat,
data.cinert.flat,
data.cvel.flat,
data.qfrc_actuator.flat,
data.cfrc_ext.flat,
]
)
def step(self, a):
self.do_simulation(a, self.frame_skip)
pos_after = self.sim.data.qpos[2]
data = self.sim.data
uph_cost = (pos_after - 0) / self.model.opt.timestep
quad_ctrl_cost = 0.1 * np.square(data.ctrl).sum()
quad_impact_cost = 0.5e-6 * np.square(data.cfrc_ext).sum()
quad_impact_cost = min(quad_impact_cost, 10)
reward = uph_cost - quad_ctrl_cost - quad_impact_cost + 1
if self.render_mode == "human":
self.render()
return (
self._get_obs(),
reward,
False,
False,
dict(
reward_linup=uph_cost,
reward_quadctrl=-quad_ctrl_cost,
reward_impact=-quad_impact_cost,
),
)
def reset_model(self):
c = 0.01
self.set_state(
self.init_qpos + self.np_random.uniform(low=-c, high=c, size=self.model.nq),
self.init_qvel
+ self.np_random.uniform(
low=-c,
high=c,
size=self.model.nv,
),
)
return self._get_obs()
def viewer_setup(self):
assert self.viewer is not None
self.viewer.cam.trackbodyid = 1
self.viewer.cam.distance = self.model.stat.extent * 1.0
self.viewer.cam.lookat[2] = 0.8925
self.viewer.cam.elevation = -20
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