The repository provides a streamlined implementation of the AlphaZero algorithm for training game-playing agents. It uses Monte Carlo Tree Search combined with deep neural networks for self-play training. Users can add custom environments by implementing a defined interface (reset, step, get_legal_actions, to_observation, etc.) and train agents through configurable episodes and simulations. It includes wandb integration for logging training metrics.