Parametrized stochastic multi-armed bandits with binary rewards

Chong Jiang, Rayadurgam Srikant

Research output: Chapter in Book/Report/Conference proceedingConference contribution


In this paper, we consider the problem of multi-armed bandits with a large number of correlated arms. We assume that the arms have Bernoulli distributed rewards, independent across time, where the probabilities of success are parametrized by known attribute vectors for each arm, as well as an unknown preference vector, each of dimension n. For this model, we seek an algorithm with a total regret that is sub-linear in time and independent of the number of arms. We present such an algorithm, which we call the Three-phase Algorithm, and analyze its performance. We show an upper bound on the total regret which applies uniformly in time. The asymptotics of this bound show that for any f ∈ ω(log(T)), the total regret can be made to be O(n·f(T)), independent of the number of arms.

Original languageEnglish (US)
Title of host publicationProceedings of the 2011 American Control Conference, ACC 2011
Number of pages6
StatePublished - 2011
Event2011 American Control Conference, ACC 2011 - San Francisco, CA, United States
Duration: Jun 29 2011Jul 1 2011


Other2011 American Control Conference, ACC 2011
Country/TerritoryUnited States
CitySan Francisco, CA

ASJC Scopus subject areas

  • Electrical and Electronic Engineering

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