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

Example Games Between Trained Models

• Each move with 400 playouts:
  
• Each move with 800 playouts:
  

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:

• Theano >= 0.7
• Lasagne >= 0.1

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:

python train.py

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.