A Linear Programming-Based Reinforcement Learning Mechanism for Incomplete-Information Games

Abstract

Dear Editor, Recently, with the development of artificial intelligence, game intelligence decision-making has attracted more and more attention. In particular, incomplete-information games (IIG) have gradually become a new research focus, where players make decisions without sufficient information, such as the opponent's strategies or preferences. In this case, a selfish player can only make reactive decisions based on the changes in environment and state. Thus, blind decisions by players may drift them away from the path of reward maximization, and may even hinder the health of the IIG environment. Therefore, it is necessary to design an effective mechanism to optimize decision-making for IIG players.