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We present a new anytime algorithm that achieves near-optimal regret for anyinstance of finite stochastic partial monitoring. In particular, the newalgorithm achieves the minimax regret, within logarithmic factors, for both"easy" and "hard" problems. For easy problems, it additionally achieveslogarithmic individual regret. Most importantly, the algorithm is adaptive inthe sense that if the opponent strategy is in an "easy region" of the strategyspace then the regret grows as if the problem was easy. As an implication, weshow that under some reasonable additional assumptions, the algorithm enjoys anO(\sqrt{T}) regret in Dynamic Pricing, proven to be hard by Bartok et al.(2011).

An Adaptive Algorithm for Finite Stochastic Partial Monitoring