Gaussian multi-armed bandit problems with multiple objectives

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

3 Scopus citations


Motivated by the goal of formally integrating human designers into computational systems for engineering design optimization, I study decision making under uncertainty with multiple objectives in the context of the multi-armed bandit problem. A key aspect of multi-objective optimization is the need for scalarization, i.e., a way to combine the various objectives into a single well-defined scalar objective function. I study the case where the multi-objective rewards are Gaussian distributed and the scalarization is linear and develop an algorithm that achieves optimal performance, i.e., converges to selecting the best arm at the highest possible rate.

Original languageEnglish (US)
Title of host publication2016 American Control Conference, ACC 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages7
ISBN (Electronic)9781467386821
StatePublished - Jul 28 2016
Externally publishedYes
Event2016 American Control Conference, ACC 2016 - Boston, United States
Duration: Jul 6 2016Jul 8 2016

Publication series

NameProceedings of the American Control Conference
ISSN (Print)0743-1619


Other2016 American Control Conference, ACC 2016
Country/TerritoryUnited States

ASJC Scopus subject areas

  • Electrical and Electronic Engineering


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