Nagi-ovo/alphazero-gomoku

AlphaZero Gomoku

An implementation of the AlphaZero algorithm for the game of Gomoku (Five in a Row), featuring self-play reinforcement learning and Monte Carlo Tree Search.

Training Loss Curve over 22 Iterations, last for 4 days on a single RTX 3090 Ti:

MCTS simulations=2000, cpuct=4.0

TODOs:

• Refactor this project and improve code quality to serve as nano alphazero
• ...

Key Features

• Complete AlphaZero algorithm implementation with MCTS and policy-value network
• Self-play training with experience replay buffer
• 1cycle learning rate scheduling for stable training
• Arena evaluation mechanism for model selection
• Support for different board sizes (9x9, 15x15)

Installation

pip install torch numpy tqdm wandb pygame

You can download the best pre-trained model so far from my huggingface repo and put it in the temp folder.

Usage

Play Against Trained Model

python alphazero.py --play \
    --round=2 \
    --player1=human \
    --player2=alphazero \
    --ckpt_file=best.pth.tar \
    --verbose

Train from Scratch

python alphazero.py --train --wandb

Key Parameters

• numMCTSSims: Number of MCTS simulations per move (default: 400)
• numEps: Number of self-play games per iteration (default: 100)
• maxlenOfQueue: Size of replay buffer (default: 200000)
• cpuct: Exploration constant in MCTS (default: 1.0)
• min_lr/max_lr: Learning rate bounds for 1cycle schedule (1e-4 to 1e-2)
• board_size: Board size, directly affects model size & training speed (default: 9)

Project Structure

• alphazero.py: Main implementation including MCTS and neural network
• game.py: Gomoku game logic & rules, including board state rendering, move generation, and game end detection

Blog & Tutorial

alphazero-from-scratch

Acknowledgments

This project is inspired by and has greatly benefited from the work of schinger/AlphaZero.