kaesve/muzero
A clean implementation of MuZero and AlphaZero following the AlphaZero General framework. Train and Pit both algorithms against each other, and investigate reliability of learned MuZero MDP models.
This project helps researchers and students understand and experiment with advanced AI decision-making algorithms like AlphaZero and MuZero. It takes configuration files defining AI agents and game environments, then trains the agents and allows them to compete. The output includes performance metrics and visualizations of how the AI models learn and make decisions, which is valuable for anyone studying or developing reinforcement learning.
168 stars. No commits in the last 6 months.
Use this if you are a researcher or student in AI and machine learning, particularly interested in how model-based reinforcement learning algorithms like MuZero operate and learn in various environments.
Not ideal if you are looking for a pre-trained, production-ready AI agent for complex board games, as this implementation focuses on research and pedagogical clarity over extensive testing on such scenarios.
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168
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27
Language
Jupyter Notebook
License
MIT
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Last pushed
Mar 28, 2021
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