Consistent Opponent Modeling in Imperfect-Information Games

Sam Ganzfried

Papers from arXiv.org

Abstract: The goal of agents in multi-agent environments is to maximize total reward against the opposing agents that are encountered. Following a game-theoretic solution concept, such as Nash equilibrium, may obtain a strong performance in some settings; however, such approaches fail to capitalize on historical and observed data from repeated interactions against our opponents. Opponent modeling algorithms integrate machine learning techniques to exploit suboptimal opponents utilizing available data; however, the effectiveness of such approaches in imperfect-information games to date is quite limited. We show that existing opponent modeling approaches fail to satisfy a simple desirable property even against static opponents drawn from a known prior distribution; namely, they do not guarantee that the model approaches the opponent's true strategy even in the limit as the number of iterations approaches infinity. We develop a new algorithm that achieves this property given sufficiently informative observations and runs efficiently by solving a convex minimization problem based on the sequence-form game representation.

Date: 2025-08, Revised 2026-10
New Economics Papers: this item is included in nep-gth
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