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import numpy as np
from gym import utils
from gym.envs.mujoco import MuJocoPyEnv
from gym.spaces import Box
class ReacherEnv(MuJocoPyEnv, utils.EzPickle):
metadata = {
"render_modes": [
"human",
"rgb_array",
"depth_array",
],
"render_fps": 50,
}
def __init__(self, **kwargs):
utils.EzPickle.__init__(self, **kwargs)
observation_space = Box(low=-np.inf, high=np.inf, shape=(11,), dtype=np.float64)
MuJocoPyEnv.__init__(
self, "reacher.xml", 2, observation_space=observation_space, **kwargs
)
def step(self, a):
vec = self.get_body_com("fingertip") - self.get_body_com("target")
reward_dist = -np.linalg.norm(vec)
reward_ctrl = -np.square(a).sum()
reward = reward_dist + reward_ctrl
self.do_simulation(a, self.frame_skip)
if self.render_mode == "human":
self.render()
ob = self._get_obs()
return (
ob,
reward,
False,
False,
dict(reward_dist=reward_dist, reward_ctrl=reward_ctrl),
)
def viewer_setup(self):
assert self.viewer is not None
self.viewer.cam.trackbodyid = 0
def reset_model(self):
qpos = (
self.np_random.uniform(low=-0.1, high=0.1, size=self.model.nq)
+ self.init_qpos
)
while True:
self.goal = self.np_random.uniform(low=-0.2, high=0.2, size=2)
if np.linalg.norm(self.goal) < 0.2:
break
qpos[-2:] = self.goal
qvel = self.init_qvel + self.np_random.uniform(
low=-0.005, high=0.005, size=self.model.nv
)
qvel[-2:] = 0
self.set_state(qpos, qvel)
return self._get_obs()
def _get_obs(self):
theta = self.sim.data.qpos.flat[:2]
return np.concatenate(
[
np.cos(theta),
np.sin(theta),
self.sim.data.qpos.flat[2:],
self.sim.data.qvel.flat[:2],
self.get_body_com("fingertip") - self.get_body_com("target"),
]
)
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