In-Context Exploiter for Extensive-Form Games
Shuxin Li, Chang Yang, Youzhi Zhang, Pengdeng Li, Xinrun Wang, Xiao, Huang, Hau Chan, Bo An

TL;DR
This paper introduces In-Context Exploiter (ICE), a novel transformer-based method that learns to exploit any opponent in extensive-form games through in-context learning, surpassing traditional Nash equilibrium strategies.
Contribution
We propose ICE, a pioneering in-context learning approach enabling a single model to adaptively exploit diverse opponents in extensive-form games.
Findings
ICE effectively exploits unknown opponents in various game scenarios.
The method outperforms traditional equilibrium-based strategies.
Experimental results demonstrate ICE's adaptability and robustness.
Abstract
Nash equilibrium (NE) is a widely adopted solution concept in game theory due to its stability property. However, we observe that the NE strategy might not always yield the best results, especially against opponents who do not adhere to NE strategies. Based on this observation, we pose a new game-solving question: Can we learn a model that can exploit any, even NE, opponent to maximize their own utility? In this work, we make the first attempt to investigate this problem through in-context learning. Specifically, we introduce a novel method, In-Context Exploiter (ICE), to train a single model that can act as any player in the game and adaptively exploit opponents entirely by in-context learning. Our ICE algorithm involves generating diverse opponent strategies, collecting interactive history training data by a reinforcement learning algorithm, and training a transformer-based agent…
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Taxonomy
TopicsArtificial Intelligence in Games · Human Motion and Animation
