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
87 lines
2.5 KiB
Python
87 lines
2.5 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 PusherEnv(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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utils.EzPickle.__init__(self)
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observation_space = Box(low=-np.inf, high=np.inf, shape=(23,), dtype=np.float64)
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mujoco_env.MujocoEnv.__init__(
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self,
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"pusher.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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def step(self, a):
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vec_1 = self.get_body_com("object") - self.get_body_com("tips_arm")
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vec_2 = self.get_body_com("object") - self.get_body_com("goal")
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reward_near = -np.linalg.norm(vec_1)
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reward_dist = -np.linalg.norm(vec_2)
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reward_ctrl = -np.square(a).sum()
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reward = reward_dist + 0.1 * reward_ctrl + 0.5 * reward_near
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self.do_simulation(a, self.frame_skip)
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self.renderer.render_step()
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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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assert self.viewer is not None
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self.viewer.cam.trackbodyid = -1
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self.viewer.cam.distance = 4.0
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def reset_model(self):
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qpos = self.init_qpos
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self.goal_pos = np.asarray([0, 0])
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while True:
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self.cylinder_pos = np.concatenate(
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[
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self.np_random.uniform(low=-0.3, high=0, size=1),
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self.np_random.uniform(low=-0.2, high=0.2, size=1),
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]
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)
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if np.linalg.norm(self.cylinder_pos - self.goal_pos) > 0.17:
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break
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qpos[-4:-2] = self.cylinder_pos
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qpos[-2:] = self.goal_pos
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qvel = self.init_qvel + self.np_random.uniform(
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low=-0.005, high=0.005, size=self.model.nv
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)
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qvel[-4:] = 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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return np.concatenate(
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[
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self.sim.data.qpos.flat[:7],
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self.sim.data.qvel.flat[:7],
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self.get_body_com("tips_arm"),
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self.get_body_com("object"),
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self.get_body_com("goal"),
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]
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)
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