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61a39f41bc
* Remove step initialization for mujoco obs spaces * remove step initialization for mujoco obs space * pre-commit pytest obs space mujoco
59 lines
1.7 KiB
Python
59 lines
1.7 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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from gym.spaces import Box
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class SwimmerEnv(mujoco_env.MujocoEnv, utils.EzPickle):
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metadata = {
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"render_modes": [
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"human",
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"rgb_array",
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"depth_array",
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"single_rgb_array",
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"single_depth_array",
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],
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"render_fps": 25,
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}
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def __init__(self, **kwargs):
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observation_space = Box(low=-np.inf, high=np.inf, shape=(8,), dtype=np.float64)
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mujoco_env.MujocoEnv.__init__(
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self,
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"swimmer.xml",
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4,
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mujoco_bindings="mujoco_py",
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observation_space=observation_space,
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**kwargs
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)
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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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self.renderer.render_step()
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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
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+ self.np_random.uniform(low=-0.1, high=0.1, size=self.model.nq),
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self.init_qvel
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+ self.np_random.uniform(low=-0.1, high=0.1, size=self.model.nv),
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)
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return self._get_obs()
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