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:
- AlphaZero: Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm
- AlphaGo Zero: Mastering the game of Go without human knowledge
- Each move with 400 playouts:
- Each move with 800 playouts:
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.
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:
- 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.
- 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.