Query-Reward Tradeoffs in Multi-Armed Bandits

Nadav Merlis; Yonathan Efroni; Shie Mannor

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arXiv · 2110.05724

Query-Reward Tradeoffs in Multi-Armed Bandits

Nadav Merlis·Yonathan Efroni·Shie Mannor

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Abstract

We consider a stochastic multi-armed bandit setting where reward must be actively queried for it to be observed. We provide tight lower and upper problem-dependent guarantees on both the regret and the number of queries. Interestingly, we prove that there is a fundamental difference between problems with a unique and multiple optimal arms, unlike in the standard multi-armed bandit problem. We also present a new, simple, UCB-style sampling concept, and show that it naturally adapts to the number of optimal arms and achieves tight regret and querying bounds.

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Nadav Merlis, Yonathan Efroni, Shie Mannor. 2021-10-12. Query-Reward Tradeoffs in Multi-Armed Bandits. https://arxiv.org/abs/2110.05724

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