Hwhitetooth/jax_muzero
An implementation of MuZero in JAX.
This project provides an advanced artificial intelligence agent, MuZero, capable of learning to master complex games and decision-making tasks without being told the rules. It takes raw observations from an environment, such as a game screen, and outputs optimal actions to achieve high scores or desired outcomes. Developers and researchers working on cutting-edge reinforcement learning applications would use this for training AI agents.
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Use this if you are a machine learning researcher or developer working with reinforcement learning and want to experiment with or apply a JAX-based implementation of the MuZero algorithm, especially for tasks like game AI or sequential decision-making.
Not ideal if you are looking for a pre-trained, plug-and-play AI solution for a specific problem or if you are not familiar with deep reinforcement learning concepts and JAX.
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Language
Python
License
MIT
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Last pushed
Nov 08, 2022
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