Improving Agent Decision Payoffs via a New Framework of Opponent Modeling

Chanjuan Liu, Jinmiao Cong, Tianhao Zhao and Enqiang Zhu ()
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Chanjuan Liu: School of Computer Science and Technology, Dalian University of Technology, Dalian 116024, China
Jinmiao Cong: School of Computer Science and Technology, Dalian University of Technology, Dalian 116024, China
Tianhao Zhao: School of Computer Science and Technology, Dalian University of Technology, Dalian 116024, China
Enqiang Zhu: Institute of Computing Science and Technology, Guangzhou University, Guangzhou 510006, China

Mathematics, 2023, vol. 11, issue 14, 1-15

Abstract: The payoff of an agent depends on both the environment and the actions of other agents. Thus, the ability to model and predict the strategies and behaviors of other agents in an interactive decision-making scenario is one of the core functionalities in intelligent systems. State-of-the-art methods for opponent modeling mainly use an explicit model of opponents’ actions, preferences, targets, etc., that the primary agent uses to make decisions. It is more important for an agent to increase its payoff than to accurately predict opponents’ behavior. Therefore, we propose a framework synchronizing the opponent modeling and decision making of the primary agent by incorporating opponent modeling into reinforcement learning. For interactive decisions, the payoff depends not only on the behavioral characteristics of the opponent but also the current state. However, confounding the two obscures the effects of state and action, which then cannot be accurately encoded. To this end, state evaluation is separated from action evaluation in our model. The experimental results from two game environments, a simulated soccer game and a real game called quiz bowl, show that the introduction of opponent modeling can effectively improve decision payoffs. In addition, the proposed framework for opponent modeling outperforms benchmark models.

Keywords: computational intelligence; opponent modeling; deep neural networks; reinforcement learning; interactive decision making (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
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