Ecosyste.ms: Awesome
An open API service indexing awesome lists of open source software.
https://github.com/kngwyu/mujoco-maze
Simple maze environments using mujoco-py
https://github.com/kngwyu/mujoco-maze
Last synced: about 1 month ago
JSON representation
Simple maze environments using mujoco-py
- Host: GitHub
- URL: https://github.com/kngwyu/mujoco-maze
- Owner: kngwyu
- License: apache-2.0
- Created: 2020-05-21T10:08:26.000Z (about 4 years ago)
- Default Branch: main
- Last Pushed: 2023-12-27T05:54:51.000Z (6 months ago)
- Last Synced: 2024-05-09T10:21:58.553Z (about 2 months ago)
- Language: Python
- Size: 794 KB
- Stars: 48
- Watchers: 2
- Forks: 12
- Open Issues: 8
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Lists
- awesome-deep-reinforcement-learning - kngwyu/mujoco-maze
README
# mujoco-maze
[](https://github.com/kngwyu/mujoco-maze/actions)
[](https://pypi.org/project/mujoco-maze/)
[](https://github.com/psf/black)
Some maze environments for reinforcement learning (RL) based on [mujoco-py]
and [openai gym][gym].
Thankfully, this project is based on the code from [rllab] and
[tensorflow/models][models].
Note that [d4rl] and [dm_control] have similar maze
environments, and you can also check them.
But, if you want more customizable or minimal one, I recommend this.
## Usage
Importing `mujoco_maze` registers environments and you can load
environments by `gym.make`.
All available environments listed are listed in [Environments] section.
E.g.,:
```python
import gym
import mujoco_maze # noqa
env = gym.make("Ant4Rooms-v0")
```
## Environments
- PointUMaze/AntUmaze/SwimmerUmaze
- PointUMaze-v0/AntUMaze-v0/SwimmerUMaze-v0 (Distance-based Reward)
- PointUmaze-v1/AntUMaze-v1/SwimmerUMaze-v (Goal-based Reward i.e., 1.0 or -ε)
- PointSquareRoom/AntSquareRoom/SwimmerSquareRoom
- PointSquareRoom-v0/AntSquareRoom-v0/SwimmerSquareRoom-v0 (Distance-based Reward)
- PointSquareRoom-v1/AntSquareRoom-v1/SwimmerSquareRoom-v1 (Goal-based Reward)
- PointSquareRoom-v2/AntSquareRoom-v2/SwimmerSquareRoom-v2 (No Reward)
- Point4Rooms/Ant4Rooms/Swimmer4Rooms
- Point4Rooms-v0/Ant4Rooms-v0/Swimmer4Rooms-v0 (Distance-based Reward)
- Point4Rooms-v1/Ant4Rooms-v1/Swimmer4Rooms-v1 (Goal-based Reward)
- Point4Rooms-v2/Ant4Rooms-v2/Swimmer4Rooms-v2 (Multiple Goals (0.5 pt or 1.0 pt))
- PointCorridor/AntCorridor/SwimmerCorridor
- PointCorridor-v0/AntCorridor-v0/SwimmerCorridor-v0 (Distance-based Reward)
- PointCorridor-v1/AntCorridor-v1/SwimmerCorridor-v1 (Goal-based Reward)
- PointCorridor-v2/AntCorridor-v2/SwimmerCorridor-v2 (No Reward)
- PointPush/AntPush
- PointPush-v0/AntPush-v0 (Distance-based Reward)
- PointPush-v1/AntPush-v1 (Goal-based Reward)
- PointFall/AntFall
- PointFall-v0/AntFall-v0 (Distance-based Reward)
- PointFall-v1/AntFall-v1 (Goal-based Reward)
- PointBilliard
- PointBilliard-v0 (Distance-based Reward)
- PointBilliard-v1 (Goal-based Reward)
- PointBilliard-v2 (Multiple Goals (0.5 pt or 1.0 pt))
- PointBilliard-v3 (Two goals (0.5 pt or 1.0 pt))
- PointBilliard-v4 (No Reward)
## Customize Environments
You can define your own task by using components in `maze_task.py`,
like:
```python
import gym
import numpy as np
from mujoco_maze.maze_env_utils import MazeCell
from mujoco_maze.maze_task import MazeGoal, MazeTask
from mujoco_maze.point import PointEnv
class GoalRewardEMaze(MazeTask):
REWARD_THRESHOLD: float = 0.9
PENALTY: float = -0.0001
def __init__(self, scale):
super().__init__(scale)
self.goals = [MazeGoal(np.array([0.0, 4.0]) * scale)]
def reward(self, obs):
return 1.0 if self.termination(obs) else self.PENALTY
@staticmethod
def create_maze():
E, B, R = MazeCell.EMPTY, MazeCell.BLOCK, MazeCell.ROBOT
return [
[B, B, B, B, B],
[B, R, E, E, B],
[B, B, B, E, B],
[B, E, E, E, B],
[B, B, B, E, B],
[B, E, E, E, B],
[B, B, B, B, B],
]
gym.envs.register(
id="PointEMaze-v0",
entry_point="mujoco_maze.maze_env:MazeEnv",
kwargs=dict(
model_cls=PointEnv,
maze_task=GoalRewardEMaze,
maze_size_scaling=GoalRewardEMaze.MAZE_SIZE_SCALING.point,
inner_reward_scaling=GoalRewardEMaze.INNER_REWARD_SCALING,
)
)
```
You can also customize models. See `point.py` or so.
## Warning
Reacher enviroments are not tested.
## [Experimental] Web-based visualizer
By passing a port like `gym.make("PointEMaze-v0", websock_port=7777)`,
one can use a web-based visualizer when calling `env.render()`.
This feature is experimental and can produce some zombie proceses.
## License
This project is licensed under Apache License, Version 2.0
([LICENSE](LICENSE) or http://www.apache.org/licenses/LICENSE-2.0).
[d4rl]: https://github.com/rail-berkeley/d4rl
[dm_control]: https://github.com/deepmind/dm_control
[gym]: https://github.com/openai/gym
[models]: https://github.com/tensorflow/models/tree/master/research/efficient-hrl
[mujoco-py]: https://github.com/openai/mujoco-py
[rllab]: https://github.com/rll/rllab