mujoco_maze/README.md

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# mujoco-maze
Some maze environments for reinforcement learning(RL) using [mujoco-py] and
[openai gym][gym].
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Thankfully, this project is based on the code from [rllab] and [tensorflow/models][models].
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## 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/PointPush.png)
- PointPush-v0/AntPush-v0 (Distance-based Reward)
- PointPush-v1/AntPush-v1 (Goal-based Reward)
- PointFall/AntFall
![PointFall](./screenshots/PointFall.png)
- PointFall-v0/AntFall-v0 (Distance-based Reward)
- PointFall-v1/AntFall-v1 (Goal-based Reward)
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## Caveats
This project has some other features (e.g., block maze and other
robots) but they are work in progress.
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## License
This project is licensed under Apache License, Version 2.0
([LICENSE-APACHE](LICENSE) or http://www.apache.org/licenses/LICENSE-2.0).
[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