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muzero-general is an open-source implementation of the MuZero reinforcement learning algorithm introduced by DeepMind. MuZero is a model-based reinforcement learning method that combines neural networks with Monte Carlo Tree Search to learn decision-making policies without requiring explicit knowledge of the environment’s dynamics. The repository provides a well-documented and commented implementation designed primarily for educational purposes. It allows researchers and developers to train reinforcement learning agents that can learn to play games such as Atari environments or board games. The framework is modular so that users can easily add new environments by defining the game logic and associated hyperparameters. It also includes support for distributed training, GPU acceleration, and monitoring tools for tracking learning progress.
Features
- Educational implementation of the MuZero reinforcement learning algorithm
- Modular framework that supports multiple game environments
- Neural network models using residual or fully connected architectures
- Self-play training combined with Monte Carlo Tree Search
- Multi-GPU and distributed training support
- Monitoring tools such as TensorBoard for training visualization
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