# junxiaosong/AlphaZero_Gomoku

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

Repository: https://github.com/junxiaosong/AlphaZero_Gomoku
Canonical: https://ross.abutalabs.com/products/alphazero_gomoku
Language: Python
License: MIT
License Family: permissive
Topics: alphazero, mcts, alphago-zero, gomoku, gobang, monte-carlo-tree-search, alphago, reinforcement-learning, rl, board-game, self-learning, pytorch, tensorflow
Last push: 2024-04-24T00:40:39+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 3177, "days_push": 862, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 3625, forks 1020 (observed 2026-08-28T04:08:11.932332+00:00)

## What it is
A Python implementation of the AlphaZero algorithm that trains a Gomoku (Five in a Row) AI purely through self-play using MCTS and a policy-value neural network. It supports PyTorch, TensorFlow, and Theano/Lasagne backends and is designed as an educational reference that can train a decent model on a single PC.

## Use cases
- learn how the AlphaZero algorithm works
- train a Gomoku AI from scratch via self-play
- play against a self-learned board game AI
- study Monte Carlo Tree Search combined with neural networks
- experiment with swapping deep learning frameworks in an RL pipeline
- reproduce AlphaGo Zero-style training on a small game

## When to choose
- you want a small, readable codebase to understand AlphaZero self-play training
- you need a Gomoku AI trainable on a single PC in hours
- you want to compare PyTorch, TensorFlow, and Theano implementations of the same RL algorithm

## When to avoid
- you need a production-strength Go, chess, or Shogi engine
- you want a polished game application with a full GUI
- you need distributed or large-scale RL training infrastructure

## Facets
- artifact type: learning-resource
- maturity: maintenance
- function: reinforcement-learning, machine-learning, deep-learning, game-engine
- domain: reinforcement-learning, artificial-intelligence, machine-learning
- platform: python, cross-platform
- tags: alphazero, mcts, gomoku, self-play, board-game, pytorch, tensorflow, theano, educational, game-development

## Member repositories
- junxiaosong/AlphaZero_Gomoku (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:08:11.932332+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-29T18:31:56.162755+00:00, confidence not recorded.
  - readme: https://github.com/junxiaosong/AlphaZero_Gomoku (fetched 2026-08-28T04:08:11.932332+00:00, sha 15f4471be2ec)
- Data as of 2026-08-30T08:39:29.467469+00:00.