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arXiv · 2212.06027

Opponent Modeling in Multiplayer Imperfect-Information Games

Sam Ganzfried·Kevin A. Wang·Max Chiswick
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Abstract

In many real-world settings agents engage in strategic interactions with multiple opposing agents who can employ a wide variety of strategies. The standard approach for designing agents for such settings is to compute or approximate a relevant game-theoretic solution concept such as Nash equilibrium and then follow the prescribed strategy. However, such a strategy ignores any observations of opponents' play, which may indicate shortcomings that can be exploited. We present an approach for opponent modeling in multiplayer imperfect-information games where we collect observations of opponents' play through repeated interactions. We run experiments against a wide variety of real opponents and exact Nash equilibrium strategies in three-player Kuhn poker and show that our algorithm significantly outperforms all of the agents, including the exact Nash equilibrium strategies.

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Sam Ganzfried, Kevin A. Wang, Max Chiswick. 2022-12-12. Opponent Modeling in Multiplayer Imperfect-Information Games. https://arxiv.org/abs/2212.06027

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