Dynamic programming with total variational distance uncertaint
2012 (English)In: Decision and Control (CDC), 2012 IEEE 51st Annual Conference on, IEEE , 2012, 1909-1914 p.Conference paper (Refereed)
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.
, IEEE Conference on Decision and Control. Proceedings, ISSN 0191-2216
Electrical Engineering, Electronic Engineering, Information Engineering
IdentifiersURN: urn:nbn:se:kth:diva-117621DOI: 10.1109/CDC.2012.6426334ScopusID: 2-s2.0-84874239408OAI: oai:DiVA.org:kth-117621DiVA: diva2:602326
51st IEEE Conference on Decision and Control, CDC 2012; Maui, HI; 10 December 2012 through 13 December 2012
QC 201303042013-01-312013-01-312013-03-04Bibliographically approved