Satisficing in Gaussian bandit problems

Paul Reverdy, Naomi E. Leonard

Research output: Contribution to journalConference articlepeer-review

2 Scopus citations


We propose a satisficing objective for the multi-armed bandit problem, i.e., where the objective is to achieve performance above a given threshold. We show that this new problem is equivalent to a standard multi-armed bandit problem with a maximizing objective and use this equivalence to find bounds on performance in terms of the satisficing objective. For the special case of Gaussian rewards we show that the satisficing problem is equivalent to a related standard multi-armed bandit problem again with Gaussian rewards. We apply the Upper Credible Limit (UCL) algorithm to this standard problem and show how it achieves optimal performance in terms of the satisficing objective.

Original languageEnglish (US)
Article number7040284
Pages (from-to)5718-5723
Number of pages6
JournalProceedings of the IEEE Conference on Decision and Control
Issue numberFebruary
StatePublished - 2014
Event2014 53rd IEEE Annual Conference on Decision and Control, CDC 2014 - Los Angeles, United States
Duration: Dec 15 2014Dec 17 2014

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Modeling and Simulation
  • Control and Optimization


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