mirror of
https://github.com/Farama-Foundation/Gymnasium.git
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2ede09074f
* Allows a new RNG to be generated with seed=-1 and updated env_checker to fix bug if environment doesn't use np_random in reset
* Revert "fixed `gym.vector.make` where the checker was being applied in the opposite case than was intended to (#2871)"
This reverts commit 519dfd9117.
* Remove bad pushed commits
* Fixed spelling in core.py
* Pins pytest to the last py 3.6 version
* Allow Box automatic scalar shape
* Add test box and change default from () to (1,)
* update Box shape inference with more strict checking
* Update the box shape and add check on the custom Box shape
* Removed incorrect shape type and assert shape code
* Update the Box and associated tests
* Remove all folders and files from pyright exclude
* Revert issues
* Push RedTachyon code review
* Add Python Platform
* Remove play from pyright check
* Fixed CI issues
* remove mujoco env type hinting
* Fixed pixel observation test
* Added some new type hints
* Fixed CI errors
* Fixed CI errors
* Remove play.py from exlucde pyright
* Fixed pyright issues
84 lines
2.4 KiB
Python
84 lines
2.4 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 AntEnv(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": 20,
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}
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def __init__(self, **kwargs):
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observation_space = Box(
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low=-np.inf, high=np.inf, shape=(111,), dtype=np.float64
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)
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mujoco_env.MujocoEnv.__init__(
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self,
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"ant.xml",
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5,
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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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xposbefore = self.get_body_com("torso")[0]
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self.do_simulation(a, self.frame_skip)
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xposafter = self.get_body_com("torso")[0]
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self.renderer.render_step()
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forward_reward = (xposafter - xposbefore) / self.dt
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ctrl_cost = 0.5 * np.square(a).sum()
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contact_cost = (
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0.5 * 1e-3 * np.sum(np.square(np.clip(self.sim.data.cfrc_ext, -1, 1)))
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)
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survive_reward = 1.0
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reward = forward_reward - ctrl_cost - contact_cost + survive_reward
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state = self.state_vector()
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notdone = np.isfinite(state).all() and state[2] >= 0.2 and state[2] <= 1.0
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done = not notdone
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ob = self._get_obs()
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return (
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ob,
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reward,
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done,
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dict(
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reward_forward=forward_reward,
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reward_ctrl=-ctrl_cost,
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reward_contact=-contact_cost,
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reward_survive=survive_reward,
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),
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)
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def _get_obs(self):
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return np.concatenate(
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[
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self.sim.data.qpos.flat[2:],
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self.sim.data.qvel.flat,
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np.clip(self.sim.data.cfrc_ext, -1, 1).flat,
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]
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)
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def reset_model(self):
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qpos = self.init_qpos + self.np_random.uniform(
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size=self.model.nq, low=-0.1, high=0.1
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)
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qvel = self.init_qvel + self.np_random.standard_normal(self.model.nv) * 0.1
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self.set_state(qpos, qvel)
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return self._get_obs()
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def viewer_setup(self):
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assert self.viewer is not None
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self.viewer.cam.distance = self.model.stat.extent * 0.5
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