Donggyu Yun, Sumyeong Ahn, A. Proutière, Jinwoo Shin, Yung Yi
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引用次数: 0
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.
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