Adapting Strategies to Opponent Models in Incomplete Information Games: A Reinforcement Learning Approach for Poker

Abstract

Researching into the incomplete information games (IIG) field requires the development of strategies which focus on optimizing the decision making process, as there is no unequivocal best choice for a particular play. As such, this paper describes the development process and testing of an agent able to compete against human players on Poker – one of the most popular IIG. The used methodology combines pre-defined opponent models with a reinforcement learning approach. The decision-making algorithm creates a different strategy against each type of opponent by identifying the opponent’s type and adjusting the rewards of the actions of the corresponding strategy. The opponent models are simple classifications used by Poker experts. Thus, each strategy is constantly adapted throughout the games, continuously improving the agent’s performance. In light of this, two agents with the same structure but different rewarding conditions were developed and tested against other agents and each other. The test results indicated that after a training phase the developed strategy is capable of outperforming basic/intermediate playing strategies thus validating this approach.