Related papers: L2E: Learning to Exploit Your Opponent

L2E: Learning to Exploit Your Opponent

URL: http://arxiv.org/abs/2102.09381v1
Date: Thu, 18 Feb 2021 14:27:59 GMT
Title: L2E: Learning to Exploit Your Opponent
Authors: Zhe Wu, Kai Li, Enmin Zhao, Hang Xu, Meng Zhang, Haobo Fu, Bo An, Junliang Xing
Abstract summary: We propose a novel Learning to Exploit framework for implicit opponent modeling. L2E acquires the ability to exploit opponents by a few interactions with different opponents during training. We propose a novel opponent strategy generation algorithm that produces effective opponents for training automatically.
Score: 66.66334543946672
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: Opponent modeling is essential to exploit sub-optimal opponents in strategic interactions. Most previous works focus on building explicit models to directly predict the opponents' styles or strategies, which require a large amount of data to train the model and lack adaptability to unknown opponents. In this work, we propose a novel Learning to Exploit (L2E) framework for implicit opponent modeling. L2E acquires the ability to exploit opponents by a few interactions with different opponents during training, thus can adapt to new opponents with unknown styles during testing quickly. We propose a novel opponent strategy generation algorithm that produces effective opponents for training automatically. We evaluate L2E on two poker games and one grid soccer game, which are the commonly used benchmarks for opponent modeling. Comprehensive experimental results indicate that L2E quickly adapts to diverse styles of unknown opponents.

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