A Relative Exponential Weighing Algorithm for Adversarial Utility-based Dueling Bandits

Pratik Gajane 1, 2 Tanguy Urvoy 2 Fabrice Clérot 2
1 SEQUEL - Sequential Learning
Inria Lille - Nord Europe, CRIStAL - Centre de Recherche en Informatique, Signal et Automatique de Lille (CRIStAL) - UMR 9189
Abstract : We study the K-armed dueling bandit problem which is a variation of the classical Multi-Armed Bandit (MAB) problem in which the learner receives only relative feedback about the selected pairs of arms. We propose a new algorithm called Relative Exponential-weight algorithm for Exploration and Exploitation (REX3) to handle the adversarial utility-based formulation of this problem. This algorithm is a non-trivial extension of the Exponential-weight algorithm for Exploration and Exploitation (EXP3) algorithm. We prove a finite time expected regret upper bound of order O(sqrt(K ln(K)T)) for this algorithm and a general lower bound of order omega(sqrt(KT)). At the end, we provide experimental results using real data from information retrieval applications.
Document type :
Conference papers
Complete list of metadatas

Cited literature [26 references]  Display  Hide  Download

https://hal.inria.fr/hal-01225614
Contributor : Pratik Gajane <>
Submitted on : Thursday, January 14, 2016 - 1:37:23 PM
Last modification on : Friday, March 22, 2019 - 1:35:17 AM
Long-term archiving on : Friday, April 15, 2016 - 2:31:39 PM

Identifiers

  • HAL Id : hal-01225614, version 1
  • ARXIV : 1601.03855

Citation

Pratik Gajane, Tanguy Urvoy, Fabrice Clérot. A Relative Exponential Weighing Algorithm for Adversarial Utility-based Dueling Bandits. Proceedings of the 32nd International Conference on Machine Learning , Jul 2015, Lille, France. pp.218-227. ⟨hal-01225614⟩

Share

Metrics

Record views

332

Files downloads

139