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From Restless to Contextual: A Thresholding Bandit Reformulation For Finite-horizon Improvement
This paper addresses the poor finite-horizon performance of existing online \emph{restless bandit} (RB) algorithms, which stems from the prohibitive sample complexity of learning a full \emph{Markov decision process} (MDP) for each agent. We argue that superior finite-horizon performance requires \e
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