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Approximating ergodic average reward continuous: time controlled Markov chains

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dc.contributor.authorLorenzo Magán, José María
dc.date.accessioned2024-10-04T11:37:14Z
dc.date.available2024-10-04T11:37:14Z
dc.date.issued2010-01
dc.description.abstractWe study the approximation of an ergodic average reward continuous-time denumerable state Markov decision process (MDP) by means of a sequence of MDPs. Our results include the convergence of the corresponding optimal policies and the optimal gains. For a controlled upwardly skip-free process, we show some computational results to illustrate the convergence theorems
dc.description.departmentDepto. de Economía Financiera y Actuarial y Estadística
dc.description.facultyFac. de Ciencias Económicas y Empresariales
dc.description.refereedTRUE
dc.description.statuspub
dc.identifier.citationT. Prieto-Rumeau and J. M. Lorenzo, "Approximating Ergodic Average Reward Continuous-Time Controlled Markov Chains," in IEEE Transactions on Automatic Control, vol. 55, no. 1, pp. 201-207, Jan. 2010, doi: 10.1109/TAC.2009.2033848. keywords: {Convergence;Optimal control;State-space methods;Statistics;Operations research;Process control;Adaptive control;Terminology;Approximation of control problems;Ergodic Markov decision processes (MDPs);policy iteration algorithm},
dc.identifier.doi10.1109/TAC.2009.2033848
dc.identifier.essn1558-2523
dc.identifier.issn0018-9286
dc.identifier.urihttps://hdl.handle.net/20.500.14352/108643
dc.issue.number1
dc.journal.titleIEEE TRANSACTIONS ON AUTOMATIC CONTROL
dc.language.isoeng
dc.page.final207
dc.page.initial201
dc.publisherIEEE
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internationalen
dc.rights.accessRightsrestricted access
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subject.keywordApproximation of control problems
dc.subject.keywordErgodic Markov decision processes (MDPs)
dc.subject.keywordPolicy iteration algorithm
dc.subject.ucmEstadística
dc.subject.unesco1209 Estadística
dc.titleApproximating ergodic average reward continuous: time controlled Markov chains
dc.typejournal article
dc.type.hasVersionVoR
dc.volume.number55
dspace.entity.typePublication
relation.isAuthorOfPublicationc1ee52ed-409c-4df3-b640-f490b9a5caa1
relation.isAuthorOfPublication.latestForDiscoveryc1ee52ed-409c-4df3-b640-f490b9a5caa1

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