# brilee/MuGo

Replicating AlphaGo's architecture in a readable manner

Repository: https://github.com/brilee/MuGo
Canonical: https://ross.abutalabs.com/products/mugo
Language: Python
License: Apache-2.0
License Family: permissive
Archived: true
Last push: 2020-03-29T12:26:02+00:00

## Health v2 (maintenance only)
Score: 10/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 0, release rhythm 8, longevity 100
- inputs: {"age_days": 3751, "days_push": 2348, "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 1163, forks 228 (observed 2026-08-28T04:03:49.667189+00:00)

## What it is
A pure Python, minimalist reimplementation of AlphaGo's architecture, including its policy and value networks and Monte Carlo tree search, built for readability. It serves as an educational reference for understanding how AlphaGo plays Go.

## Use cases
- understand how AlphaGo's architecture works
- learn to implement Monte Carlo tree search with neural networks
- build a Go engine in Python
- train a policy network on Go game records
- study neural network game playing as a learning project

## When to choose
- you want a readable, educational implementation of AlphaGo's design
- you are studying deep learning applied to board games
- you want a starting point to understand MCTS with policy and value networks

## When to avoid
- you need a strong, actively maintained Go engine
- you want production-quality performance
- you need continued development or support - the project is abandoned since 2017

## Facets
- artifact type: learning-resource
- maturity: abandoned
- function: machine-learning, deep-learning, game-engine, simulation
- domain: artificial-intelligence, deep-learning, tutorials
- platform: python, cross-platform
- tags: go-engine, alphago, reinforcement-learning, mcts, tensorflow, board-games, educational, game-development, gpu

## Member repositories
- brilee/MuGo (main) score 10

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:03:49.667189+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 2026-08-30T06:31:33.399725+00:00, confidence not recorded.
  - readme: https://github.com/brilee/MuGo (fetched 2026-08-28T04:03:49.667189+00:00, sha 5509e13f3049)
- Data as of 2026-08-30T08:39:29.467469+00:00.