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arXiv · 2106.02436

Stochastic Multi-Armed Bandits with Unrestricted Delay Distributions

Tal Lancewicki·Shahar Segal·Tomer Koren·Yishay Mansour
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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 reward-dependent delay setting, where realized delays may depend on the stochastic rewards, and the 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 infinite delays where the algorithm might occasionally not observe any feedback.

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Tal Lancewicki, Shahar Segal, Tomer Koren, Yishay Mansour. 2021-06-04. Stochastic Multi-Armed Bandits with Unrestricted Delay Distributions. https://arxiv.org/abs/2106.02436

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