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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 | |