Learning in Games: Robustness of Fast Convergence

NeurIPS 2016 Dylan J. FosterZhiyuan LiThodoris LykourisKarthik SridharanEva Tardos

We show that learning algorithms satisfying a $\textit{low approximate regret}$ property experience fast convergence to approximate optimality in a large class of repeated games. Our property, which simply requires that each learner has small regret compared to a $(1+\epsilon)$-multiplicative approximation to the best action in hindsight, is ubiquitous among learning algorithms; it is satisfied even by the vanilla Hedge forecaster... (read more)

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