GomokuZeroAI

GomokuZeroAI is an AlphaZero-style Gomoku checkpoint trained with self-play, a PyTorch policy-value network, and Monte Carlo Tree Search.

This repository hosts the model weights used by the companion project:

https://github.com/maojh15/GomokuZeroAI

The current checkpoint is:

iter_0150_15x15.pt

It is intended for local human-vs-AI play through the project's web UI.

Quick Start

Clone the code repository:

git clone https://github.com/maojh15/GomokuZeroAI.git
cd GomokuZeroAI

Install dependencies:

pip install numpy torch pyyaml huggingface_hub

Download the checkpoint:

hf download maojh15/GomokuZeroAI iter_0150_15x15.pt --local-dir result_15x15/checkpoints

Start the local human-vs-AI server:

python play_human.py --host 127.0.0.1 --port 8765

Open the web UI:

http://127.0.0.1:8765

Select iter_0150_15x15.pt in the checkpoint dropdown and click the new-game button to start playing.

Model Details

• Game: Gomoku / Five in a Row
• Board size: 15x15
• Checkpoint: iter_0150_15x15.pt
• Training iteration: 150
• Framework: PyTorch
• Architecture: convolutional policy-value network
• Input channels: 2
• Network width: 128 channels
• Player encodings: 1 and -1
• MCTS backend used during training: C++ Torch Extension
• MCTS playouts during training: 2000
• Opening self-play temperature: 1.0 for the first 12 moves
• Evaluation temperature: 0.001 after the opening

The network predicts:

• a policy distribution over legal board moves
• a value estimate in [0, 1] from the current player's perspective

The local web UI can display both the raw network value and the MCTS root value.

Intended Use

This checkpoint is meant for:

• playing Gomoku against the AI locally
• inspecting policy and visit overlays in the web UI
• comparing future GomokuZeroAI checkpoints
• experimenting with AlphaZero-style self-play training code

This is not a Transformers model and is not intended for use through the Hugging Face pipeline() API.

Limitations

• The model was trained for 15x15 Gomoku only.
• It requires the GomokuZeroAI codebase to load and run correctly.
• Playing strength depends heavily on the MCTS playout setting used at inference time.
• Higher playouts usually improve move quality but increase latency.
• The checkpoint is an experimental game AI model, not a benchmarked tournament engine.

Recommended Inference Settings

For interactive human-vs-AI play, start with:

• MCTS playouts: 2000
• c_puct: 5.0
• candidate distance: empty / all legal moves
• mcts_tactical_shortcuts: enabled for faster tactical responses in the web UI

If moves are too slow on your machine, reduce MCTS playouts to 400-1000.

Files

iter_0150_15x15.pt

This file contains the model weights and training configuration payload used by the GomokuZeroAI checkpoint loader.

Citation

If you use this checkpoint or codebase in your own experiments, please reference the project repository:

https://github.com/maojh15/GomokuZeroAI