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dc.contributor.authorSaldı, Naci
dc.contributor.authorYuksel, S.
dc.contributor.authorLinder, T.
dc.date.accessioned2021-02-10T12:28:01Z
dc.date.available2021-02-10T12:28:01Z
dc.date.issued2020-01
dc.identifier.issn0018-9286en_US
dc.identifier.urihttp://hdl.handle.net/10679/7292
dc.identifier.urihttps://ieeexplore.ieee.org/document/8673573
dc.description.abstractWe consider finite model approximations of discrete-time partially observed Markov decision processes (POMDPs) under the discounted cost criterion. After converting the original partially observed stochastic control problem to a fully observed one on the belief space, the finite models are obtained through the uniform quantization of the state and action spaces of the belief space Markov decision process (MDP). Under mild assumptions on the components of the original model, it is established that the policies obtained from these finite models are nearly optimal for the belief space MDP, and so, for the original partially observed problem. The assumptions essentially require that the belief space MDP satisfies a mild weak continuity condition. We provide an example and introduce explicit approximation procedures for the quantization of the set of probability measures on the state space of POMDP (i.e., belief space).en_US
dc.description.sponsorshipNatural Sciences and Engineering Research Council of Canada (NSERC)
dc.language.isoengen_US
dc.publisherIEEEen_US
dc.relation.ispartofIEEE Transactions on Automatic Control
dc.rightsrestrictedAccess
dc.titleAsymptotic optimality of finite model approximations for partially observed markov decision processes with discounted costen_US
dc.typeArticleen_US
dc.peerreviewedyesen_US
dc.publicationstatusPublisheden_US
dc.contributor.departmentÖzyeğin University
dc.contributor.authorID(ORCID 0000-0002-2677-7366 & YÖK ID 283091) Saldı, Naci
dc.contributor.ozuauthorSaldı, Naci
dc.identifier.volume65en_US
dc.identifier.issue1en_US
dc.identifier.startpage130en_US
dc.identifier.endpage142en_US
dc.identifier.wosWOS:000506851100010
dc.identifier.doi10.1109/TAC.2019.2907172en_US
dc.subject.keywordsAerospace electronicsen_US
dc.subject.keywordsConvergenceen_US
dc.subject.keywordsQuantization (signal)en_US
dc.subject.keywordsMarkov processesen_US
dc.subject.keywordsComputational modelingen_US
dc.subject.keywordsCost functionen_US
dc.subject.keywordsApproximationsen_US
dc.subject.keywordsMarkov decision processesen_US
dc.subject.keywordsNon-linear filteringen_US
dc.subject.keywordsQuantizationen_US
dc.subject.keywordsStochastic controlen_US
dc.identifier.scopusSCOPUS:2-s2.0-85077786832
dc.contributor.authorMale1
dc.relation.publicationcategoryArticle - International Refereed Journal - Institutional Academic Staff


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