  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.
