Surveillance in an abruptly changing world via multiarmed bandits

Vaibhav Srivastava, Paul Reverdy, Naomi E. Leonard

Research output: Contribution to journalConference article

11 Scopus citations

Abstract

We study a path planning problem in an environment that is abruptly changing due to the arrival of unknown spatial events. The objective of the path planning problem is to collect the data that is most evidential about the events. We formulate this problem as a multiarmed bandit (MAB) problem with Gaussian rewards and change points, and address the fundamental tradeoff between learning the true event (exploration), and collecting the data that is most evidential about the true event (exploitation). We extend the switching-window UCB algorithm for MAB problems with bounded rewards and change points to the context of correlated Gaussian rewards and develop the switching-window UCL (SW-UCL) algorithm. We extend the SW-UCL algorithm to an adaptive SW-UCL algorithm that utilizes statistical change detection to adapt the SW-UCL algorithm. We also develop a block SW-UCL algorithm that reduces the number of transitions among arms in the SW-UCL algorithm, and is more amenable to robotic applications.

Original languageEnglish (US)
Article number7039462
Pages (from-to)692-697
Number of pages6
JournalProceedings of the IEEE Conference on Decision and Control
Volume2015-February
Issue numberFebruary
DOIs
StatePublished - Jan 1 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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