32 lines
1.1 KiB
Python
32 lines
1.1 KiB
Python
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 SwimmerEnv(mujoco_env.MujocoEnv, utils.EzPickle):
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def __init__(self):
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mujoco_env.MujocoEnv.__init__(self, 'swimmer.xml', 4)
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utils.EzPickle.__init__(self)
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def step(self, a):
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ctrl_cost_coeff = 0.0001
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xposbefore = self.sim.data.qpos[0]
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self.do_simulation(a, self.frame_skip)
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xposafter = self.sim.data.qpos[0]
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reward_fwd = (xposafter - xposbefore) / self.dt
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reward_ctrl = - ctrl_cost_coeff * np.square(a).sum()
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reward = reward_fwd + reward_ctrl
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ob = self._get_obs()
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return ob, reward, False, dict(reward_fwd=reward_fwd, reward_ctrl=reward_ctrl)
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def _get_obs(self):
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qpos = self.sim.data.qpos
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qvel = self.sim.data.qvel
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return np.concatenate([qpos.flat[2:], qvel.flat])
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def reset_model(self):
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self.set_state(
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self.init_qpos + self.np_random.uniform(low=-.1, high=.1, size=self.model.nq),
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self.init_qvel + self.np_random.uniform(low=-.1, high=.1, size=self.model.nv)
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)
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return self._get_obs()
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