Best Arm Identification: A Unified Approach to Fixed Budget and Fixed Confidence - Inria - Institut national de recherche en sciences et technologies du numérique Accéder directement au contenu
Communication Dans Un Congrès Année : 2012

Best Arm Identification: A Unified Approach to Fixed Budget and Fixed Confidence

Mohammad Ghavamzadeh
  • Fonction : Auteur
  • PersonId : 868946
Alessandro Lazaric

Résumé

We study the problem of identifying the best arm(s) in the stochastic multi-armed bandit setting. This problem has been studied in the literature from two different perspectives: {\em fixed budget} and {\em fixed confidence}. We propose a unifying approach that leads to a meta-algorithm called unified gap-based exploration (UGapE), with a common structure and similar theoretical analysis for these two settings. We prove a performance bound for the two versions of the algorithm showing that the two problems are characterized by the same notion of complexity. We also show how the UGapE algorithm as well as its theoretical analysis can be extended to take into account the variance of the arms and to multiple bandits. Finally, we evaluate the performance of UGapE and compare it with a number of existing fixed budget and fixed confidence algorithms.
Fichier principal
Vignette du fichier
nips2012l.pdf (443.25 Ko) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)
Loading...

Dates et versions

hal-00772615 , version 1 (10-01-2013)

Identifiants

  • HAL Id : hal-00772615 , version 1

Citer

Victor Gabillon, Mohammad Ghavamzadeh, Alessandro Lazaric. Best Arm Identification: A Unified Approach to Fixed Budget and Fixed Confidence. NIPS - Twenty-Sixth Annual Conference on Neural Information Processing Systems, Dec 2012, Lake Tahoe, United States. ⟨hal-00772615⟩
275 Consultations
247 Téléchargements

Partager

Gmail Facebook X LinkedIn More