A Implementation of Alpha Zero for Chess in MLX

In plain words

This is a chess engine built using MLX for neural network computations combined with Monte Carlo Tree Search for selecting moves, following the AlphaZero approach. It is designed for developers and chess enthusiasts interested in AI-driven game playing systems. The implementation leverages MLX's efficient computation capabilities to train and run neural networks that evaluate chess positions alongside MCTS algorithms to explore possible move sequences.

written from the facts on this page · September 2026

From the sources

time to learn mlx. Contribute to koogle/mlx-playground development by creating an account on GitHub.

A chess engine implementation inspired by AlphaZero, using MLX for neural network computations and Monte Carlo Tree Search (MCTS) for movefrom github.com

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