Multi-armed bandit with additional observations

Abstract

We study multi-armed bandit (MAB) problems with additional observations, where in each round, the decision maker selects an arm to play and can also observe rewards of additional arms (within a given budget) by paying certain costs. We propose algorithms that are asymptotic-optimal and order-optimal in their regrets under the settings of stochastic and adversarial rewards, respectively.