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Algorithms for Infinitely Many-Armed Bandits Department of Statistics -University of Michigan
 

Summary: Algorithms for Infinitely Many-Armed Bandits
Yizao Wang
Department of Statistics - University of Michigan
437 West Hall, 1085 South University, Ann Arbor, MI, 48109-1107, USA
yizwang@umich.edu
Jean-Yves Audibert
Certis - Ecole des Ponts, 6 av. Blaise Pascal, Cité Descartes, 77455 Marne-la-Vallée, France
Willow - ENS / INRIA, 45 rue d'Ulm, 75005 Paris, France
audibert@certis.enpc.fr
Rémi Munos
INRIA Lille - Nord Europe, SequeL project,
40 avenue Halley, 59650 Villeneuve d'Ascq, France
remi.munos@inria.fr
Abstract
We consider multi-armed bandit problems where the number of arms is larger
than the possible number of experiments. We make a stochastic assumption on
the mean-reward of a new selected arm which characterizes its probability of be-
ing a near-optimal arm. Our assumption is weaker than in previous works. We
describe algorithms based on upper-confidence-bounds applied to a restricted set
of randomly selected arms and provide upper-bounds on the resulting expected

  

Source: Audibert, Jean-Yves - Département d'Informatique, École Normale Supérieure

 

Collections: Computer Technologies and Information Sciences