Learning in Games: Robustness of Fast Convergence

neural information processing systems, pp. 4727-4735, 2016.

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Abstract:

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 hindsigh...More

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