Google Research

Stochastic Multi-Armed Bandits with Unrestricted Delay Distributions

ICML 2021 (2021) (to appear)

Abstract

We study the stochastic Multi-Armed Bandit~(MAB) problem with random delays in the feedback received by the algorithm. We consider two settings: the {\it reward-dependent} delay setting, where realized delays may depend on the stochastic rewards, and the {\it reward-independent} delay setting. Our main contribution is algorithms that achieve near-optimal regret in each of the settings, with an additional additive dependence on the quantiles of the delay distribution. Our results do not make any assumptions on the delay distributions: in particular, we do not assume they come from any parametric family of distributions and allow for unbounded support and expectation; we further allow for the case of infinite delays where the algorithm might occasionally not observe any feedback.

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