Application And Challenges of Alphazero Algorithm in Chinese Chess

Abstract

In the field of artificial intelligence, AlphaZero has achieved significant success in games like Go and Shogi. However, its application in Chinese Chess remains relatively unexplored. This study aims to unveil the potential and limitations of AlphaZero in Chinese Chess and explore strategies for optimizing its performance. In this paper, we begin by reviewing the fundamental principles and algorithmic structure of AlphaZero, emphasizing its deep reinforcement learning and self-play characteristics. Subsequently, we delve into the specific application of AlphaZero in Chinese Chess, encompassing aspects such as chess rule representation, training procedures, position evaluation, and strategy prediction. By analyzing successful instances of AlphaZero in Chinese Chess, we showcase its potential in the domain of board games. Furthermore, this paper elucidates the challenges that AlphaZero encounters in the context of Chinese Chess, including issues related to computational complexity and position representation. We particularly underscore the potential cost escalation due to high computational resource requirements and the risk of data loss or excessive time consumption associated with extended computation periods. IN conclusion, this research provides valuable insights into the application of AlphaZero in the realm of Chinese Chess and offers recommendations for future research directions to further expand its utility in this domain.