DHDev0/Muzero-unplugged

Pytorch Implementation of MuZero Unplugged for gym environment. This algorithm is capable of supporting a wide range of action and observation spaces, including both discrete and continuous variations.

36
/ 100
Emerging

This project helps machine learning researchers and reinforcement learning practitioners train AI agents for complex environments, even when full simulators are unavailable or too slow. It takes expert demonstrations or previously generated agent experiences as input and produces a trained AI model capable of making decisions and achieving goals within a given environment. It's designed for those developing advanced AI for games, simulations, or control tasks.

No commits in the last 6 months.

Use this if you need to train a reinforcement learning agent for environments where you can provide expert play data or leverage past agent experiences, reducing reliance on real-time simulation.

Not ideal if you are a beginner in reinforcement learning, as this is an advanced implementation of a specific algorithm rather than a general-purpose introductory tool.

reinforcement-learning AI-training game-AI offline-RL decision-making
Stale 6m No Package No Dependents
Maintenance 2 / 25
Adoption 7 / 25
Maturity 16 / 25
Community 11 / 25

How are scores calculated?

Stars

35

Forks

4

Language

Python

License

GPL-3.0

Last pushed

Jun 25, 2025

Commits (30d)

0

Get this data via API

curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/DHDev0/Muzero-unplugged"

Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.