Opponent Modeling in Multiplayer Imperfect-Information Games

• URL: http://arxiv.org/abs/2212.06027v4
• Date: Mon, 29 Jul 2024 04:07:44 GMT
• Title: Opponent Modeling in Multiplayer Imperfect-Information Games
• Authors: Sam Ganzfried, Kevin A. Wang, Max Chiswick,
• Abstract summary: We present an approach for opponent modeling in multiplayer imperfect-information games. We run experiments against a variety of real opponents and exact Nash equilibrium strategies in three-player Kuhn poker. Our algorithm significantly outperforms all of the agents, including the exact Nash equilibrium strategies.
• Score: 1.024113475677323
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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