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On Bayesian index policies for sequential resource allocation

Emilie Kaufmann 1
1 SEQUEL - Sequential Learning
Inria Lille - Nord Europe, CRIStAL - Centre de Recherche en Informatique, Signal et Automatique de Lille (CRIStAL) - UMR 9189
Abstract : This paper is about index policies for minimizing (frequentist) regret in a stochastic multi-armed bandit model, inspired by a Bayesian view on the problem. Our main contribution is to prove that the Bayes-UCB algorithm, which relies on quantiles of posterior distributions, is asymptotically optimal when the reward distributions belong to a one-dimensional exponential family, for a large class of prior distributions. We also show that the Bayesian literature gives new insight on what kind of exploration rates could be used in frequentist, UCB-type algorithms. Indeed, approximations of the Bayesian optimal solution or the Finite Horizon Gittins indices provide a justification for the kl-UCB+ and kl-UCB-H+ algorithms, whose asymptotic optimality is also established.
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Contributor : Emilie Kaufmann <>
Submitted on : Monday, November 6, 2017 - 9:52:16 AM
Last modification on : Wednesday, October 14, 2020 - 3:47:20 AM
Long-term archiving on: : Wednesday, February 7, 2018 - 1:19:05 PM


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  • HAL Id : hal-01251606, version 3
  • ARXIV : 1601.01190


Emilie Kaufmann. On Bayesian index policies for sequential resource allocation. Annals of Statistics, Institute of Mathematical Statistics, In press. ⟨hal-01251606v3⟩



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