This project applies the AlphaGo Zero methodology to Chinese chess, using deep neural networks combined with Monte Carlo Tree Search and self-play reinforcement learning to train a competitive chess-playing agent. The system supports distributed training across multiple machines for scalable compute, and tracks ELO ratings to measure progress toward human intermediate+ skill levels.