PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

A Finite-Time Analysis of Multi-armed Bandits Problems with Kullback-Leibler Divergences
Odalric-Ambrym Maillard, Rémi Munos and Gilles Stoltz
In: COLT 2011, 9-11 July 2011, Budapest, Hungary.

Abstract

We consider a Kullback-Leibler-based algorithm for the stochastic multi-armed bandit problem in the case of distributions with finite supports (not necessarily known beforehand), whose asymptotic regret matches the lower bound of \cite{Burnetas96}. Our contribution is to provide a finite-time analysis of this algorithm; we get bounds whose main terms are smaller than the ones of previously known algorithms with finite-time analyses (like UCB-type algorithms).

PDF - Requires Adobe Acrobat Reader or other PDF viewer.
EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Learning/Statistics & Optimisation
Theory & Algorithms
ID Code:8655
Deposited By:Gilles Stoltz
Deposited On:18 February 2012