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Dynamic programming with total variational distance uncertaint
University of Cyprus. (Electrical and Computer Engineering Department)
University of Cyprus. (Electrical and Computer Engineering Department)
KTH, School of Electrical Engineering (EES), Automatic Control.
2012 (English)In: Decision and Control (CDC), 2012 IEEE 51st Annual Conference on, IEEE , 2012, 1909-1914 p.Conference paper, Published paper (Refereed)
Abstract [en]

The aim of this paper is to address optimality of stochastic control strategies via dynamic programming subject to total variational distance uncertainty on the conditional distribution of the controlled process. Utilizing concepts from signed measures, the maximization of a linear functional on the space of probability measures on abstract spaces is investigated, among those probability measures which are within a total variational distance from a nominal probability measure. The maximizing probability measure is found in closed form. These results are then applied to solve minimax stochastic control with deterministic control strategies, under a Markovian assumption on the conditional distributions of the controlled process. The results include: 1) Optimization subject to total variational distance constraints, 2) new dynamic programming recursions, which involve the oscillator seminorm of the value function.

Place, publisher, year, edition, pages
IEEE , 2012. 1909-1914 p.
Series
IEEE Conference on Decision and Control. Proceedings, ISSN 0191-2216
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:kth:diva-117621DOI: 10.1109/CDC.2012.6426334Scopus ID: 2-s2.0-84874239408OAI: oai:DiVA.org:kth-117621DiVA: diva2:602326
Conference
51st IEEE Conference on Decision and Control, CDC 2012; Maui, HI; 10 December 2012 through 13 December 2012
Note

QC 20130304

Available from: 2013-01-31 Created: 2013-01-31 Last updated: 2013-03-04Bibliographically approved

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