Jiu fusion artificial intelligence (JFA): a two-stage reinforcement learning model with hierarchical neural networks and human knowledge for Tibetan Jiu chess

• JFA is a two-stage DRL model for Tibetan Jiu chess with separate strategic layout and hierarchical battle sub-models, enabling phase-specific learning. • Knowledge-guided pruning and auxiliary agents reduce layout decision time to approximately 1/147 of AlphaZero while achieving a 74% win rate. • Combined SLM and HBM achieve an 81% win rate against other models, comparable to a human amateur 4-dan player, with HBM alone at 70%. • JFA won first place at the 2024 China National Computer Game Tournament, demonstrating robust performance under limited hardware resources.