# NeymarL/ChineseChess-AlphaZero

Implement AlphaZero/AlphaGo Zero methods on Chinese chess.

Repository: https://github.com/NeymarL/ChineseChess-AlphaZero
Canonical: https://ross.abutalabs.com/products/chinesechess-alphazero
Homepage: https://cczero.org
Language: Python
License: GPL-3.0
License Family: copyleft
Topics: reinforcement-learning, alphazero, deep-learning, chinese-chess
Last push: 2023-03-24T22:26:39+00:00

## Health v2 (maintenance only)
Score: 23/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 0, release rhythm 8, longevity 100
- inputs: {"age_days": 3137, "days_push": 1258, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1242, forks 363 (observed 2026-08-28T04:04:06.476366+00:00)

## What it is
An open-source implementation of AlphaZero/AlphaGo Zero reinforcement learning methods applied to Chinese chess (Xiangqi). It includes self-play, training, evaluation, and supervised learning workers, plus a built-in pygame GUI for playing against the trained AI.

## Use cases
- train a Chinese chess AI with AlphaZero methods
- run self-play to generate reinforcement learning training data
- play Xiangqi against a neural network with MCTS
- learn how AlphaZero works by studying a full implementation
- train a board game AI from scratch without human game data
- evaluate new model generations against a best model

## When to choose
- you want to build or study an AlphaZero-style AI for Chinese chess
- you need a reference implementation of self-play, MCTS, and policy/value network training
- you want to contribute compute to a distributed Chinese chess training project

## When to avoid
- you need a production-strength Xiangqi engine for competitive play
- you work with modern TensorFlow/PyTorch versions, since the stack targets old Python 3.6 and TensorFlow 1.x
- you want AlphaZero for a different game like Go or chess without porting

## Facets
- artifact type: library
- maturity: maintenance
- function: reinforcement-learning, deep-learning, machine-learning, game-engine
- domain: reinforcement-learning, deep-learning, artificial-intelligence
- platform: python, cross-platform
- tags: alphazero, chinese-chess, mcts, self-play, tensorflow, keras, board-games, game-development

## Member repositories
- NeymarL/ChineseChess-AlphaZero (main) score 23

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:04:06.476366+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 2026-08-30T05:08:23.085168+00:00, confidence not recorded.
  - readme: https://github.com/NeymarL/ChineseChess-AlphaZero (fetched 2026-08-28T04:04:06.476366+00:00, sha 61de21a67c9d)
  - homepage: https://cczero.org (fetched 2026-08-29T12:20:20.977076+00:00, sha 44136fa355b3)
- Data as of 2026-08-30T08:39:29.467469+00:00.