mujoco_maze/README.md

# mujoco-maze
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Some maze environments for reinforcement learning(RL) using [mujoco-py] and
[openai gym][gym].

Thankfully, this project is based on the code from  [rllab] and
[tensorflow/models][models].

Note that [d4rl][d4rl] and [dm_control][dm_control] have similar maze
environment, 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 environments listed
below are available via `gym.make`.

E.g.,
```python
import gym
import mujoco_maze  # noqa
env = gym.make("Ant4Rooms-v0")
```

## Environments

- PointUMaze/AntUmaze

  ![PointUMaze](./screenshots/PointUMaze.png)
  - PointUMaze-v0/AntUMaze-v0 (Distance-based Reward)
  - PointUmaze-v1/AntUMaze-v1 (Goal-based Reward i.e., 1.0 or -ε)

- Point4Rooms/Ant4Rooms

  ![Point4Rooms](./screenshots/Point4Rooms.png)
  - Point4Rooms-v0/Ant4Rooms-v0 (Distance-based Reward)
  - Point4Rooms-v1/Ant4Rooms-v1 (Goal-based Reward)
  - Point4Rooms-v2/Ant4Rooms-v2 (Multiple Goals (0.5 pt or 1.0 pt))

- PointPush/AntPush

  ![PointPush](./screenshots/AntPush.png)
  - PointPush-v0/AntPush-v0 (Distance-based Reward)
  - PointPush-v1/AntPush-v1 (Goal-based Reward)

- PointFall/AntFall

  ![PointFall](./screenshots/AntFall.png)
  - PointFall-v0/AntFall-v0 (Distance-based Reward)
  - PointFall-v1/AntFall-v1 (Goal-based Reward)

- PointBilliard

  ![PointBilliard](./screenshots/PointBilliard.png)
  - PointBilliard-v0 (Distance-based Reward)
  - PointBilliard-v1 (Goal-based Reward)
  - PointBilliard-v2 (Multiple Goals (0.5 pt or 1.0 pt))

## Customize Environments
You can define your own task by using components in `maze_task.py`,
like:

```
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
This project has some other environments (e.g., reacher and swimmer)
but if they are not on README, they are work in progress and
not tested well.

## License
This project is licensed under Apache License, Version 2.0
([LICENSE-APACHE](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