# tensorflow/minigo

An open-source implementation of the AlphaGoZero algorithm

Repository: https://github.com/tensorflow/minigo
Canonical: https://ross.abutalabs.com/products/minigo
Language: C++
License: Apache-2.0
License Family: permissive
Archived: true
Last push: 2021-03-04T17:42:02+00:00

## Health v2 (maintenance only)
Score: 10/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 0, release rhythm 8, longevity 100
- inputs: {"age_days": 3138, "days_push": 2008, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: archived
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 3542, forks 561 (observed 2026-08-28T04:08:09.266645+00:00)

## What it is
Minigo is an open-source, minimalist implementation of the AlphaGo Zero algorithm for the game of Go, built on TensorFlow. It provides a readable reinforcement learning pipeline for self-play training, evaluation, and serving of Go models, with tooling for Kubernetes and Google Cloud Platform.

## Use cases
- reproduce the AlphaGo Zero algorithm from scratch
- train a neural network Go engine via self-play reinforcement learning
- learn how to build RL pipelines with TensorFlow and Kubernetes
- run a Go AI engine to play against or analyze games
- study a readable implementation of AlphaZero-style training
- benchmark RL training on GPUs and TPUs

## When to choose
- you want a clear, educational implementation of AlphaGo Zero rather than a maximally competitive engine
- you want to experiment with self-play RL pipelines on cloud infrastructure
- you need open training data and models for Go research
- you want to extend or adapt an AlphaZero-style architecture for other games

## When to avoid
- you need the strongest possible Go AI for serious play or analysis
- you want a lightweight engine without TensorFlow or GPU dependencies
- you need actively maintained software with recent releases
- you want a general-purpose game AI framework rather than a Go-specific one

## Facets
- artifact type: library
- maturity: maintenance
- function: machine-learning, deep-learning, reinforcement-learning, game-engine, simulation
- domain: artificial-intelligence, machine-learning, reinforcement-learning, education
- platform: python, cloud
- tags: go-engine, alphago-zero, self-play, tensorflow, board-games, mcts, neural-network, game-development, linux, docker, gpu

## Member repositories
- tensorflow/minigo (main) score 10

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:08:09.266645+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 2026-08-29T18:34:28.910366+00:00, confidence not recorded.
  - readme: https://github.com/tensorflow/minigo (fetched 2026-08-28T04:08:09.266645+00:00, sha 9c819dd46997)
- Data as of 2026-08-30T08:39:29.467469+00:00.