Lightning does not strike twice: Robust MDPs with coupled uncertainty

Shie Mannor, Ofir Mebel, Huan Xu

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

We consider Markov decision processes under parameter uncertainty. Previous studies all restrict to the case that uncertainties among different states are uncoupled, which leads to conservative solutions. In contrast, we introduce an intuitive concept, termed "Lightning Does not Strike Twice," to model coupled uncertain parameters. Specifically, we require that the system can deviate from its nominal parameters only a bounded number of times. We give probabilistic guarantees indicating that this model represents real life situations and devise tractable algorithms for computing optimal control policies.

Original languageEnglish
Title of host publicationProceedings of the 29th International Conference on Machine Learning, ICML 2012
Pages385-392
Number of pages8
StatePublished - 2012
Event29th International Conference on Machine Learning, ICML 2012 - Edinburgh, United Kingdom
Duration: 26 Jun 20121 Jul 2012

Publication series

NameProceedings of the 29th International Conference on Machine Learning, ICML 2012
Volume1

Conference

Conference29th International Conference on Machine Learning, ICML 2012
Country/TerritoryUnited Kingdom
CityEdinburgh
Period26/06/121/07/12

All Science Journal Classification (ASJC) codes

  • Human-Computer Interaction
  • Education

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