Strategizing against No-regret Learners
Yuan Deng, Jon Schneider, Balasubramanian Sivan
Abstract
How should a player who repeatedly plays a game against a no-regret learner strategize to maximize his utility? We study this question and show that under some mild assumptions, the player can always guarantee himself a utility of at least what he would get in a Stackelberg equilibrium. When the no-regret learner has only two actions, we show that the player cannot get any higher utility than the Stackelberg equilibrium utility. But when the no-regret learner has more than two actions and plays a mean-based no-regret strategy, we show that the player can get strictly higher than the Stackelberg equilibrium utility. We construct the optimal game-play for the player against a mean-based no-regret learner who has three actions. When the no-regret learner's strategy also guarantees him a no-swap regret, we show that the player cannot get anything higher than a Stackelberg equilibrium utility.
BibTeX
@inproceedings{NEURIPS2019_8b6dd7db,
author = {Deng, Yuan and Schneider, Jon and Sivan, Balasubramanian},
booktitle = {Advances in Neural Information Processing Systems},
editor = {H. Wallach and H. Larochelle and A. Beygelzimer and F. d\textquotesingle Alch\'{e}-Buc and E. Fox and R. Garnett},
pages = {},
publisher = {Curran Associates, Inc.},
title = {Strategizing against No-regret Learners},
url = {https://proceedings.neurips.cc/paper_files/paper/2019/file/8b6dd7db9af49e67306feb59a8bdc52c-Paper.pdf},
volume = {32},
year = {2019}
}