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https://github.com/junxiaosong/AlphaZero_Gomoku

An implementation of the AlphaZero algorithm for Gomoku (also called Gobang or Five in a Row)
https://github.com/junxiaosong/AlphaZero_Gomoku

alphago alphago-zero alphazero board-game gobang gomoku mcts monte-carlo-tree-search pytorch reinforcement-learning rl self-learning tensorflow

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An implementation of the AlphaZero algorithm for Gomoku (also called Gobang or Five in a Row)

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## AlphaZero-Gomoku

This is an implementation of the AlphaZero algorithm for playing the simple board game Gomoku (also called Gobang or Five in a Row) from pure self-play training. The game Gomoku is much simpler than Go or chess, so that we can focus on the training scheme of AlphaZero and obtain a pretty good AI model on a single PC in a few hours. 



References:  

1. AlphaZero: Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm

2. AlphaGo Zero: Mastering the game of Go without human knowledge



### Update 2018.2.24: supports training with TensorFlow!

### Update 2018.1.17: supports training with PyTorch!



### Example Games Between Trained Models

- Each move with 400 MCTS playouts:  

![playout400](https://raw.githubusercontent.com/junxiaosong/AlphaZero_Gomoku/master/playout400.gif)



### Requirements

To play with the trained AI models, only need:

- Python >= 2.7

- Numpy >= 1.11



To train the AI model from scratch, further need, either:

- Theano >= 0.7 and Lasagne >= 0.1      

or

- PyTorch >= 0.2.0    

or

- TensorFlow



**PS**: if your Theano's version > 0.7, please follow this [issue](https://github.com/aigamedev/scikit-neuralnetwork/issues/235) to install Lasagne,  

otherwise, force pip to downgrade Theano to 0.7 ``pip install --upgrade theano==0.7.0``



If you would like to train the model using other DL frameworks, you only need to rewrite policy_value_net.py.



### Getting Started

To play with provided models, run the following script from the directory:  

```

python human_play.py  

```

You may modify human_play.py to try different provided models or the pure MCTS.



To train the AI model from scratch, with Theano and Lasagne, directly run:   

```

python train.py

```

With PyTorch or TensorFlow, first modify the file [train.py](https://github.com/junxiaosong/AlphaZero_Gomoku/blob/master/train.py), i.e., comment the line

```

from policy_value_net import PolicyValueNet  # Theano and Lasagne

```

and uncomment the line 

```

# from policy_value_net_pytorch import PolicyValueNet  # Pytorch

or

# from policy_value_net_tensorflow import PolicyValueNet # Tensorflow

```

and then execute: ``python train.py``  (To use GPU in PyTorch, set ``use_gpu=True`` and use ``return loss.item(), entropy.item()`` in function train_step in policy_value_net_pytorch.py if your pytorch version is greater than 0.5)



The models (best_policy.model and current_policy.model) will be saved every a few updates (default 50).  



**Note:** the 4 provided models were trained using Theano/Lasagne, to use them with PyTorch, please refer to [issue 5](https://github.com/junxiaosong/AlphaZero_Gomoku/issues/5).



**Tips for training:**

1. It is good to start with a 6 * 6 board and 4 in a row. For this case, we may obtain a reasonably good model within 500~1000 self-play games in about 2 hours.

2. For the case of 8 * 8 board and 5 in a row, it may need 2000~3000 self-play games to get a good model, and it may take about 2 days on a single PC.



### Further reading

My article describing some details about the implementation in Chinese: [https://zhuanlan.zhihu.com/p/32089487](https://zhuanlan.zhihu.com/p/32089487)