/AlphaZero_Gomoku

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

Primary LanguagePythonMIT LicenseMIT

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:
    playout400
  • Each move with 800 playouts:
    playout800

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

PS: if your Theano's version > 0.7, please follow this issue 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, such as TensorFlow or PyTorch, 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, run:

python train.py

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

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.