muzero-general and muzero
The kaesve implementation is a specialized fork/reimplementation of the werner-duvaud framework that adds MuZero-specific features (learned MDP models, inter-algorithm comparison) while maintaining compatibility with the AlphaZero General architecture, making them ecosystem variants rather than true competitors or complements.
About muzero-general
werner-duvaud/muzero-general
MuZero
Implements the DeepMind MuZero algorithm using PyTorch with residual and fully-connected networks, enabling model-based RL without environment dynamics knowledge. Supports distributed training across multiple GPUs and Ray clusters with asynchronous self-play, plus real-time TensorBoard monitoring. Designed for quick adaptation to new environments—board games, Atari, and OpenAI Gym—by simply defining game classes and hyperparameters.
About muzero
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.
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