Strategizing against Learners in Bayesian Games

@inproceedings{Mansour2022StrategizingAL,
  title={Strategizing against Learners in Bayesian Games},
  author={Y. Mansour and Mehryar Mohri and Jon Schneider and Balasubramanian Sivan},
  booktitle={Annual Conference Computational Learning Theory},
  year={2022}
}
We study repeated two-player games where one of the players, the learner, employs a no-regret learning strategy, while the other, the optimizer, is a rational utility maximizer. We consider general Bayesian games, where the payoffs of both the optimizer and the learner could depend on the type, which is drawn from a publicly known distribution, but revealed privately to the learner. We address the following questions: (a) what is the bare minimum that the optimizer can guarantee to obtain… 

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