A scaled down version of AlphaGo Zero, playing on a 5x5 board

This projects aims to reimplement the AlphaGo Zero paper to play on a 5x5 board.

AlphaGo Zero uses end-to-end reinforcement learning to learn Go from scratch, without any human player data.

• GoGame: Go simulation code.

• MCTS: Implmentation for Monte-carlo tree search.

• Model: Neural network architecture

• Selfplay: Module for managing the games between agents

• Shared: Miscellaneous functions and constants

• Training: Class for generating gameplay data and performing training and evaluation

Generate games and train the model:

python3 train.py

Test a trained model:

python3 test.py