# werner-duvaud/muzero-general

MuZero

Repository: https://github.com/werner-duvaud/muzero-general
Canonical: https://ross.abutalabs.com/products/muzero-general
Homepage: https://github.com/werner-duvaud/muzero-general/wiki/MuZero-Documentation
Language: Python
License: MIT
License Family: permissive
Topics: muzero, reinforcement-learning, alphazero, pytorch, python3, self-learning, monte-carlo-tree-search, deep-learning, deep-reinforcement-learning, neural-network, model-based-rl, rl, muzero-general, tensorboard, gym, mcts, alphago, residual-network, machine-learning
Last push: 2024-09-03T21:32:07+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-03T02:39:23.370411+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 2442, "days_push": 729, "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 2861, forks 671 (observed 2026-08-28T04:07:26.231510+00:00)

## What it is
A commented and documented PyTorch implementation of DeepMind's MuZero reinforcement learning algorithm, designed to be easily adapted to any game or Gym environment by adding a game file with hyperparameters. It is primarily intended for educational purposes and supports single and two-player games, multi-GPU training, and TensorBoard monitoring.

## Use cases
- train a MuZero agent on chess or Go
- apply model-based reinforcement learning to a custom Gym environment
- learn how MuZero works from a documented implementation
- run self-play training on Atari games
- experiment with Monte Carlo tree search and learned environment models
- add a new game to a reinforcement learning framework

## When to choose
- you want a readable, educational MuZero implementation to study or extend
- you need to adapt a state-of-the-art RL algorithm to a new game or Gym environment
- you want multi-GPU or Ray-cluster support for self-play training
- you prefer PyTorch and TensorBoard monitoring

## When to avoid
- you need a production-grade, highly optimized RL library for large-scale training
- you want a simple plug-and-play solution without writing a game configuration file
- you need algorithms other than MuZero/AlphaZero-style model-based RL

## Facets
- artifact type: library
- maturity: active
- function: reinforcement-learning, machine-learning, deep-learning, simulation
- domain: reinforcement-learning, machine-learning, deep-learning
- platform: python, windows
- tags: muzero, alphazero, mcts, monte-carlo-tree-search, model-based-rl, pytorch, gym, self-play, educational, game-development, linux, macos, gpu

## Member repositories
- werner-duvaud/muzero-general (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:07:26.231510+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 2026-08-29T18:48:29.959090+00:00, confidence not recorded.
  - readme: https://github.com/werner-duvaud/muzero-general (fetched 2026-08-28T04:07:26.231510+00:00, sha fc324c58c8c1)
  - homepage: https://github.com/werner-duvaud/muzero-general/wiki/MuZero-Documentation (fetched 2026-08-29T09:52:05.013706+00:00, sha a0c539599543)
- Data as of 2026-08-30T08:39:29.467469+00:00.