Stochastic online learning under unknown time-varying models

An online learning problem under stochastic time-varying models is considered. The problem is treated as a generalization of the classic multi-armed bandit problem when the arm distributions are time-varying. The objective is to study the impact of time variation in arm distributions on the performance of the player´s strategy. Sufficient conditions on the rate of model variations under which learning can or cannot improve the regret order are established.