# jonathan-laurent/AlphaZero.jl

A generic, simple and fast implementation of Deepmind's AlphaZero algorithm.

Repository: https://github.com/jonathan-laurent/AlphaZero.jl
Canonical: https://ross.abutalabs.com/products/alphazerojl
Homepage: https://jonathan-laurent.github.io/AlphaZero.jl/stable/
Language: Julia
License: MIT
License Family: permissive
Topics: alphazero, julia, machine-learning, deep-learning
Last push: 2026-04-11T00:52:58+00:00

## Health v2 (maintenance only)
Score: 64/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 76, release rhythm 28, longevity 100
- inputs: {"age_days": 2556, "days_push": 145, "days_rel": 264, "gap_med": null, "n_releases_24m": 1}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1333, forks 146 (observed 2026-08-28T04:04:24.657630+00:00)

## What it is
A generic, simple, and fast Julia implementation of DeepMind's AlphaZero algorithm for training game-playing agents via self-play and MCTS. It is designed to be accessible to students and researchers while being fast enough to solve nontrivial games on a desktop computer with a GPU.

## Use cases
- train an AlphaZero agent for Connect Four
- implement AlphaZero for a custom board game
- run reinforcement learning self-play experiments on a single GPU
- research combining learning and tree search
- reproduce AlphaZero results without a large compute cluster
- learn how the AlphaZero algorithm works internally

## When to choose
- you want a hackable, readable AlphaZero implementation in pure Julia
- you need to train agents for nontrivial games on limited hardware
- you want generic interfaces to plug in new games or learning frameworks
- you need distributed training across a cluster without code changes

## When to avoid
- you need a production-strength engine optimized for distributed C++/GPU clusters like Leela Zero
- your project is Python-based and you need JAX or PyTorch integration
- you are working outside two-player perfect-information games

## Facets
- artifact type: library
- maturity: active
- function: machine-learning, deep-learning, reinforcement-learning, gpu-computing
- domain: reinforcement-learning, machine-learning, deep-learning
- platform: windows, cross-platform
- tags: alphazero, mcts, self-play, game-ai, reinforcement-learning, game-development, julia, gpu, linux, macos

## Member repositories
- jonathan-laurent/AlphaZero.jl (main) score 64

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:04:24.657630+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 2026-08-30T04:44:47.099681+00:00, confidence not recorded.
  - readme: https://github.com/jonathan-laurent/AlphaZero.jl (fetched 2026-08-28T04:04:24.657630+00:00, sha 6d570fb7cf02)
  - homepage: https://jonathan-laurent.github.io/AlphaZero.jl/stable/ (fetched 2026-08-29T12:03:56.788867+00:00, sha a0fba232a651)
- Data as of 2026-08-30T08:39:29.467469+00:00.