The Advantage of Board Game with Deep Reinforcement Learning and Causal Inference

Abstract

With the continuous progress of artificial intelligence technology, the deep reinforcement learning method combining behaviourism and connectionism has shown fantastic performance far beyond the human level in chess games. This paper systematically introduces two mainstream chess games, including the standard algorithms of artificial intelligence technology in chess and Go, including Monte Carlo and deep reinforcement learning, and expounds on their algorithm principles and the core reasons for their strong learning ability. In addition, this paper discusses the limitations of existing methods in interpretability and puts forward the possibility of applying causal reasoning to deep reinforcement learning to solve interpretable problems. This study will provide a valuable reference for practitioners in related fields.