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arXiv · 1910.04959

Regret Bounds for Batched Bandits

Hossein Esfandiari·Amin Karbasi·Abbas Mehrabian·Vahab Mirrokni
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Abstract

We present simple and efficient algorithms for the batched stochastic multi-armed bandit and batched stochastic linear bandit problems. We prove bounds for their expected regrets that improve over the best-known regret bounds for any number of batches. In particular, our algorithms in both settings achieve the optimal expected regrets by using only a logarithmic number of batches. We also study the batched adversarial multi-armed bandit problem for the first time and find the optimal regret, up to logarithmic factors, of any algorithm with predetermined batch sizes.

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Hossein Esfandiari, Amin Karbasi, Abbas Mehrabian, Vahab Mirrokni. 2019-10-11. Regret Bounds for Batched Bandits. https://arxiv.org/abs/1910.04959

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