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import numpy as np
from gym import utils
from gym.envs.mujoco import MuJocoPyEnv
from gym.spaces import Box
class SwimmerEnv(MuJocoPyEnv, utils.EzPickle):
metadata = {
"render_modes": [
"human",
"rgb_array",
"depth_array",
],
"render_fps": 25,
}
def __init__(self, **kwargs):
observation_space = Box(low=-np.inf, high=np.inf, shape=(8,), dtype=np.float64)
MuJocoPyEnv.__init__(
self, "swimmer.xml", 4, observation_space=observation_space, **kwargs
)
utils.EzPickle.__init__(self, **kwargs)
def step(self, a):
ctrl_cost_coeff = 0.0001
xposbefore = self.sim.data.qpos[0]
self.do_simulation(a, self.frame_skip)
xposafter = self.sim.data.qpos[0]
reward_fwd = (xposafter - xposbefore) / self.dt
reward_ctrl = -ctrl_cost_coeff * np.square(a).sum()
reward = reward_fwd + reward_ctrl
ob = self._get_obs()
if self.render_mode == "human":
self.render()
return (
ob,
reward,
False,
False,
dict(reward_fwd=reward_fwd, reward_ctrl=reward_ctrl),
)
def _get_obs(self):
qpos = self.sim.data.qpos
qvel = self.sim.data.qvel
return np.concatenate([qpos.flat[2:], qvel.flat])
def reset_model(self):
self.set_state(
self.init_qpos
+ self.np_random.uniform(low=-0.1, high=0.1, size=self.model.nq),
self.init_qvel
+ self.np_random.uniform(low=-0.1, high=0.1, size=self.model.nv),
)
return self._get_obs()
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