An Adaptive Algorithm for Finite Stochastic Partial Monitoring

Understand

We present a new anytime algorithm that achieves near-optimal regret for any instance of finite stochastic partial monitoring.

• In particular, the new algorithm achieves the minimax regret, within logarithmic factors, for both "easy" and "hard" problems.
• For easy problems, it additionally achieves logarithmic individual regret.
• Most importantly, the algorithm is adaptive in the sense that if the opponent strategy is in an "easy region" of the strategy space then the regret grows as if the problem was easy.