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# MuZero General

A commented and [documented](https://github.com/werner-duvaud/muzero-general/wiki/MuZero-Documentation) implementation of MuZero based on the Google DeepMind [paper](https://arxiv.org/abs/1911.08265) (Nov 2019) and the associated [pseudocode](https://arxiv.org/src/1911.08265v2/anc/pseudocode.py).
It is designed to be easily adaptable for every games or reinforcement learning environments (like [gym](https://github.com/openai/gym)). You only need to add a [game file](https://github.com/werner-duvaud/muzero-general/tree/master/games) with the hyperparameters and the game class. Please refer to the [documentation](https://github.com/werner-duvaud/muzero-general/wiki/MuZero-Documentation) and the [example](https://github.com/werner-duvaud/muzero-general/blob/master/games/cartpole.py).

MuZero is a state of the art RL algorithm for board games (Chess, Go, ...) and Atari games.
It is the successor to [AlphaZero](https://arxiv.org/abs/1712.01815) but without any knowledge of the environment underlying dynamics. MuZero learns a model of the environment and uses an internal representation that contains only the useful information for predicting the reward, value, policy and transitions. MuZero is also close to [Value prediction networks](https://arxiv.org/abs/1707.03497). See [How it works](https://github.com/werner-duvaud/muzero-general/wiki/How-MuZero-works).

## Features

* [x] Residual Network and Fully connected network in [PyTorch](https://github.com/pytorch/pytorch)
* [x] Multi-Threaded/Asynchronous/[Cluster](https://docs.ray.io/en/latest/cluster-index.html) with [Ray](https://github.com/ray-project/ray)
* [X] Multi GPU support for the training and the selfplay
* [x] TensorBoard real-time monitoring
* [x] Model weights automatically saved at checkpoints
* [x] Single and two player mode
* [x] Commented and [documented](https://github.com/werner-duvaud/muzero-general/wiki/MuZero-Documentation)
* [x] Easily adaptable for new games
* [x] [Examples](https://github.com/werner-duvaud/muzero-general/blob/master/games/cartpole.py) of board games, Gym and Atari games (See [list of implemented games](https://github.com/werner-duvaud/muzero-general#games-already-implemented))
* [x] [Pretrained weights](https://github.com/werner-duvaud/muzero-general/tree/master/results) available
* [ ] Windows support (Experimental / Workaround: Use the [notebook](https://github.com/werner-duvaud/muzero-general/blob/master/notebook.ipynb) in [Google Colab](https://colab.research.google.com))

### Further improvements
These improvements are active research, they are personal ideas and go beyond MuZero paper. We are open to contributions and other ideas.

* [x] [Hyperparameter search](https://github.com/werner-duvaud/muzero-general/wiki/Hyperparameter-Optimization)
* [x] [Continuous action space](https://github.com/werner-duvaud/muzero-general/tree/continuous)
* [x] [Tool to understand the learned model](https://github.com/werner-duvaud/muzero-general/blob/master/diagnose_model.py)
* [ ] Support of stochastic environments
* [ ] Support of more than two player games
* [ ] RL tricks (Never Give Up,  Adaptive Exploration, ...)

## Demo

All performances are tracked and displayed in real time in [TensorBoard](https://www.tensorflow.org/tensorboard) :

![cartpole training summary](https://github.com/werner-duvaud/muzero-general/blob/master/docs/cartpole-training-summary.png)

Testing Lunar Lander :

![lunarlander training preview](https://github.com/werner-duvaud/muzero-general/blob/master/docs/lunarlander-training-preview.png)

## Games already implemented

* Cartpole      (Tested with the fully connected network)
* Lunar Lander  (Tested in deterministic mode with the fully connected network)
* Gridworld     (Tested with the fully connected network)
* Tic-tac-toe   (Tested with the fully connected network and the residual network)
* Connect4      (Slightly tested with the residual network)
* Gomoku
* Twenty-One / Blackjack    (Tested with the residual network)
* Atari Breakout

Tests are done on Ubuntu with 16 GB RAM / Intel i7 / GTX 1050Ti Max-Q. We make sure to obtain a progression and a level which ensures that it has learned. But we do not systematically reach a human level. For certain environments, we notice a regression after a certain time. The proposed configurations are certainly not optimal and we do not focus for now on the optimization of hyperparameters. Any help is welcome.

## Code structure

![code structure](https://github.com/werner-duvaud/muzero-general/blob/master/docs/code-structure-werner-duvaud.png)

Network summary:

<p align="center">
<a href="https://github.com/werner-duvaud/muzero-general/blob/master/docs/muzero-network-werner-duvaud.png">
<img src="https://github.com/werner-duvaud/muzero-general/blob/master/docs/muzero-network-werner-duvaud.png" width="250"/>
</a>
</p>

## Getting started
### Installation

```bash
git clone https://github.com/werner-duvaud/muzero-general.git
cd muzero-general

pip install -r requirements.txt
```

### Run

```bash
python muzero.py
```
To visualize the training results, run in a new terminal:
```bash
tensorboard --logdir ./results
```

## Authors

* Werner Duvaud
* Aurèle Hainaut
* Paul Lenoir
* [Contributors](https://github.com/werner-duvaud/muzero-general/graphs/contributors)

Please use this bibtex if you want to cite this repository (master branch) in your publications:
```bash
@misc{muzero-general,
  author       = {Werner Duvaud, Aurèle Hainaut},
  title        = {MuZero General: Open Reimplementation of MuZero},
  year         = {2019},
  publisher    = {GitHub},
  journal      = {GitHub repository},
  howpublished = {\url{https://github.com/werner-duvaud/muzero-general}},
}
```

## Getting involved

* [GitHub Issues](https://github.com/werner-duvaud/muzero-general/issues): For reporting bugs.
* [Pull Requests](https://github.com/werner-duvaud/muzero-general/pulls): For submitting code contributions.
* [Discord server](https://discord.gg/GB2vwsF): For discussions about development or any general questions.
