Near-OptimalNo-RegretLearningDynamicsfor GeneralConvexGames

A recent line of work has established uncoupled learning dynamics such that, when employed by all players in a game, each player's regret after T repetitions grows polylogarithmically in T, an exponential improvement over the traditional guarantees within the no-regret framework. However, so far these results have only been limited to certain classes of games with structured strategy spaces--such as normal-form and extensive-form games. The question as to whether O(polylogT) regret bounds can be obtained for general convex and compact strategy sets--which occur in many fundamental models in economics and multiagent systems--while retaining efficient strategy updates is an importantquestion.