We show that natural classes of regularized learning algorithms with a form of recency bias achieve faster convergence rates to approximate efficiency and to coarse correlated equilibria in multiplayer normal form games. When each player in a game uses an algorithm from our class, their individual regret decays at O(T\u22123\/4), while the sum of utilities converges to an approximate optimum at O(T\u22121)–an improvement upon the worst case O(T\u22121\/2) rates. We show a black-box reduction for any algorithm in the class to achieve O~(T\u22121\/2) rates against an adversary, while maintaining the faster rates against algorithms in the class. Our results extend those of [Rakhlin and Shridharan 2013] and [Daskalakis et al. 2014], who only analyzed two-player zero-sum games for specific algorithms. <\/p>\n","protected":false},"excerpt":{"rendered":"
We show that natural classes of regularized learning algorithms with a form of recency bias achieve faster convergence rates to approximate efficiency and to coarse correlated equilibria in multiplayer normal form games. 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