Research on Game Theory Based on Accurate Evaluation Function

In this paper, we delve into the application of accurate evaluation functions in game theory, emphasizing their abilities in dealing with uncertainty and incomplete information faced during decision-making process. Traditional models such as Bayesian and Markov models are deficient in being subjected to prior selection, high computational complexity, and difficulties in adapting to dynamic environments. To overcome these constraints, we propose a novel approach that integrates statistical methods, Bayesian inference, and time-series modeling to construct and refine prior distributions dynamically. Improving the accuracy, learning efficiency, and adaptability of the evaluation function, our method enhances the efficiency and reliability of decision-making. Additionally, we examine our algorithm by using reinforcement learning in the Atari Arcade Learning Environment (ALE), indicating its effectiveness in risk assessment, payoff evaluation, and strategy optimization. Finally, the experimental results confirmed our method's efficiency and reliability, offering a more accurate and flexible framework for decision-making in game theory.