Skip to yearly menu bar Skip to main content


Poster

Strategizing against No-regret Learners

Yuan Deng · Jon Schneider · Balasubramanian Sivan

East Exhibition Hall B, C #222

Keywords: [ Online Learning ] [ Algorithms ] [ Theory ] [ Game Theory and Computational Economics ]


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.

Live content is unavailable. Log in and register to view live content