Regret Bounds for Batched Bandits

Authors

  • Hossein Esfandiari Google Research, New York
  • Amin Karbasi Yale University
  • Abbas Mehrabian McGill University
  • Vahab Mirrokni Google Research, New York

Keywords:

Online Learning & Bandits, Learning Theory

Abstract

We present simple algorithms for batched stochastic multi-armed bandit and batched stochastic linear bandit problems. We prove bounds for their expected regrets that improve and extend the best known regret bounds of Gao, Han, Ren, and Zhou (NeurIPS 2019), 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 provide the optimal regret, up to logarithmic factors, of any algorithm with predetermined batch sizes.

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Published

2021-05-18

How to Cite

Esfandiari, H., Karbasi, A., Mehrabian, A., & Mirrokni, V. (2021). Regret Bounds for Batched Bandits. Proceedings of the AAAI Conference on Artificial Intelligence, 35(8), 7340-7348. Retrieved from https://ojs.aaai.org/index.php/AAAI/article/view/16901

Issue

Section

AAAI Technical Track on Machine Learning I