Fast Convergence of Regularized Learning in Games Alekh Agarwal Microsoft Research Microsoft Research New York, NY Robert E. Schapire Princeton University Microsoft Research Princeton, NJ

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.