128 lines
3.8 KiB
Markdown
128 lines
3.8 KiB
Markdown
# mujoco-maze
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[](https://github.com/kngwyu/mujoco-maze/actions)
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[](https://pypi.org/project/mujoco-maze/)
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[](https://github.com/psf/black)
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Some maze environments for reinforcement learning (RL) based on [mujoco-py]
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and [openai gym][gym].
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Thankfully, this project is based on the code from [rllab] and
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[tensorflow/models][models].
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Note that [d4rl] and [dm_control] have similar maze
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environments, and you can also check them.
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But, if you want more customizable or minimal one, I recommend this.
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## Usage
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Importing `mujoco_maze` registers environments and you can load
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environments by `gym.make`.
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All available environments listed are listed in [Environments] section.
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E.g.,:
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```python
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import gym
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import mujoco_maze # noqa
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env = gym.make("Ant4Rooms-v0")
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```
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## Environments
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- PointUMaze/AntUmaze
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- PointUMaze-v0/AntUMaze-v0 (Distance-based Reward)
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- PointUmaze-v1/AntUMaze-v1 (Goal-based Reward i.e., 1.0 or -ε)
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- Point4Rooms/Ant4Rooms
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- Point4Rooms-v0/Ant4Rooms-v0 (Distance-based Reward)
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- Point4Rooms-v1/Ant4Rooms-v1 (Goal-based Reward)
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- Point4Rooms-v2/Ant4Rooms-v2 (Multiple Goals (0.5 pt or 1.0 pt))
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- PointPush/AntPush
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- PointPush-v0/AntPush-v0 (Distance-based Reward)
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- PointPush-v1/AntPush-v1 (Goal-based Reward)
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- PointFall/AntFall
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- PointFall-v0/AntFall-v0 (Distance-based Reward)
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- PointFall-v1/AntFall-v1 (Goal-based Reward)
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- PointBilliard
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- PointBilliard-v0 (Distance-based Reward)
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- PointBilliard-v1 (Goal-based Reward)
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- PointBilliard-v2 (Multiple Goals (0.5 pt or 1.0 pt))
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## Customize Environments
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You can define your own task by using components in `maze_task.py`,
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like:
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```python
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import gym
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import numpy as np
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from mujoco_maze.maze_env_utils import MazeCell
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from mujoco_maze.maze_task import MazeGoal, MazeTask
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from mujoco_maze.point import PointEnv
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class GoalRewardEMaze(MazeTask):
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REWARD_THRESHOLD: float = 0.9
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PENALTY: float = -0.0001
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def __init__(self, scale):
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super().__init__(scale)
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self.goals = [MazeGoal(np.array([0.0, 4.0]) * scale)]
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def reward(self, obs):
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return 1.0 if self.termination(obs) else self.PENALTY
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@staticmethod
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def create_maze():
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E, B, R = MazeCell.EMPTY, MazeCell.BLOCK, MazeCell.ROBOT
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return [
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[B, B, B, B, B],
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[B, R, E, E, B],
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[B, B, B, E, B],
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[B, E, E, E, B],
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[B, B, B, E, B],
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[B, E, E, E, B],
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[B, B, B, B, B],
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]
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gym.envs.register(
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id="PointEMaze-v0",
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entry_point="mujoco_maze.maze_env:MazeEnv",
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kwargs=dict(
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model_cls=PointEnv,
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maze_task=GoalRewardEMaze,
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maze_size_scaling=GoalRewardEMaze.MAZE_SIZE_SCALING.point,
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inner_reward_scaling=GoalRewardEMaze.INNER_REWARD_SCALING,
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)
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)
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```
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You can also customize models. See `point.py` or so.
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## Warning
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This project has some other environments (e.g., reacher and swimmer)
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but if they are not on README, they are work in progress and
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not tested well.
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## License
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This project is licensed under Apache License, Version 2.0
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([LICENSE](LICENSE) or http://www.apache.org/licenses/LICENSE-2.0).
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[d4rl]: https://github.com/rail-berkeley/d4rl
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[dm_control]: https://github.com/deepmind/dm_control
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[gym]: https://github.com/openai/gym
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[models]: https://github.com/tensorflow/models/tree/master/research/efficient-hrl
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[mujoco-py]: https://github.com/openai/mujoco-py
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[rllab]: https://github.com/rll/rllab
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