45 lines
1.6 KiB
Python
45 lines
1.6 KiB
Python
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import numpy as np
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from gym import utils
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from gym.envs.mujoco import mujoco_env
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class ReacherALREnv(mujoco_env.MujocoEnv, utils.EzPickle):
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def __init__(self):
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utils.EzPickle.__init__(self)
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mujoco_env.MujocoEnv.__init__(self, '/home/vien/git/reacher_test/reacher/envs/reacher_5links.xml', 2)
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def step(self, a):
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vec = self.get_body_com("fingertip")-self.get_body_com("target")
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reward_dist = - np.linalg.norm(vec)
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reward_ctrl = - np.square(a).sum()
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reward = reward_dist + reward_ctrl
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self.do_simulation(a, self.frame_skip)
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ob = self._get_obs()
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done = False
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return ob, reward, done, dict(reward_dist=reward_dist, reward_ctrl=reward_ctrl)
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def viewer_setup(self):
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self.viewer.cam.trackbodyid = 0
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def reset_model(self):
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qpos = self.np_random.uniform(low=-0.1, high=0.1, size=self.model.nq) + self.init_qpos
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while True:
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self.goal = self.np_random.uniform(low=-.2, high=.2, size=2)
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if np.linalg.norm(self.goal) < 0.2:
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break
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qpos[-2:] = self.goal
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qvel = self.init_qvel + self.np_random.uniform(low=-.005, high=.005, size=self.model.nv)
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qvel[-2:] = 0
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self.set_state(qpos, qvel)
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return self._get_obs()
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def _get_obs(self):
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theta = self.sim.data.qpos.flat[:5]
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return np.concatenate([
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np.cos(theta),
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np.sin(theta),
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self.sim.data.qpos.flat[5:], # this is goal position
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self.sim.data.qvel.flat[:5], # this is angular velocity
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self.get_body_com("fingertip") - self.get_body_com("target")
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])
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