English

Strategizing against No-regret Learners

Computer Science and Game Theory 2025-11-12 v2 Machine Learning

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 of the game. 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 provide a characterization of the optimal game-play for the player against a mean-based no-regret learner as a solution to a control problem. 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.

Keywords

Cite

@article{arxiv.1909.13861,
  title  = {Strategizing against No-regret Learners},
  author = {Yuan Deng and Jon Schneider and Balusubramanian Sivan},
  journal= {arXiv preprint arXiv:1909.13861},
  year   = {2025}
}

Comments

11 pages. NeurIPS 2019

R2 v1 2026-06-23T11:30:35.897Z