DHDev0/Stochastic-muzero
Pytorch Implementation of Stochastic MuZero for gym environment. This algorithm is capable of supporting a wide range of action and observation spaces, including both discrete and continuous variations.
This project helps reinforcement learning researchers and practitioners train AI agents to play a wide variety of games or solve decision-making problems. You input data from a 'game' or environment, and it outputs a trained AI agent capable of making optimal decisions. It's designed for those developing or researching advanced AI agents for complex environments.
Use this if you are an AI researcher or machine learning engineer looking to implement and experiment with advanced model-based reinforcement learning algorithms like Stochastic MuZero for complex simulated environments.
Not ideal if you need a simple, off-the-shelf solution for basic reinforcement learning tasks or if you are not comfortable with advanced machine learning concepts and environment setup.
Stars
77
Forks
12
Language
Python
License
GPL-3.0
Category
Last pushed
Dec 31, 2025
Commits (30d)
0
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