2016
Learning in Games: Robustness of Fast Convergence
NeurIPS 2016poster
We show that learning algorithms satisfying a 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+eps)-multiplicative approximation to the bes…