Optimization of Alpha-Beta algorithm of Jiu Chess by combining empirical knowledge and deep reinforcement learning
Zangzu Jiu Chess, as a traditional board game, has highly complex rule systems and ever-changing board configurations.Traditional gaming strategies are unstable and perform poorly when faced with different opponents and situations, necessitating the development of new approaches to enhance the gaming capabilities of Zangzu Jiu Chess AI.This paper focuses on Zangzu Jiu Chess and, during the board layout phase, improves the traditional Alpha-Beta pruning search algorithm.It integrates empirical knowledge with deep reinforcement learning algorithms to make informed choices for rational piece placement on the chessboard, thereby paving the way for subsequent stages.During the chess stage and the flying stage, the Alpha-Beta algorithm is employed in conjunction with empirical knowledge to determine movement paths.Finally, the previously mentioned algorithms and strategies are integrated into a Zangzu Jiu Chess AI program, which achieves favorable results in the China Computer Game Championship, validating the effectiveness of this approach.
Zangzu Jiu Chessempirical knowledgeAlpha-Beta algorithmdeep reinforcement learningcomputer game
国家科技期刊平台
NSTL
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