Mastering Chinese Chess AI (Xiangqi) Without Search
<|reference_start|>Mastering Chinese Chess AI (Xiangqi) Without Search: We have developed a high-performance Chinese Chess AI that operates without reliance on search algorithms. This AI has demonstrated the capability to compete at a level commensurate with the top 0.1\% of human players. By eliminating the search process typically associated with such systems, this AI achieves a Queries Per Second (QPS) rate that exceeds those of systems based on the Monte Carlo Tree Search (MCTS) algorithm by over a thousandfold and surpasses those based on the AlphaBeta pruning algorithm by more than a hundredfold. The AI training system consists of two parts: supervised learning and reinforcement learning. Supervised learning provides an initial human-like Chinese chess AI, while reinforcement learning, based on supervised learning, elevates the strength of the entire AI to a new level. Based on this training system, we carried out enough ablation experiments and discovered that 1. The same parameter amount of Transformer architecture has a higher performance than CNN on Chinese chess; 2. Possible moves of both sides as features can greatly improve the training process; 3. Selective opponent pool, compared to pure self-play training, results in a faster improvement curve and a higher strength limit. 4. Value Estimation with Cutoff(VECT) improves the original PPO algorithm training process and we will give the explanation.<|reference_end|>
@article{chen2024mastering,
    title={Mastering Chinese Chess AI (Xiangqi) Without Search},
    author={Yu Chen, Juntong Lin, Zhichao Shu},
    journal={arXiv preprint arXiv:2410.04865},
    year={2024},
    archivePrefix={arXiv},
    eprint={2410.04865},
    primaryClass={cs.LG cs.AI}
}