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Risk Sensitive Control of Markov Processes in Countable State Space

dc.contributor.authorHernandez-Hernandez, Danielen_US
dc.contributor.authorMarcus, Steven I.en_US
dc.description.abstractIn this paper we consider infinite horizon risk-sensitive control of Markov processes with discrete time and denumerable state space. This problem is solved proving, under suitable conditions, that there exists a bounded solution to the dynamic programming equation. The dynamic programming equation is transformed into an Isaacs equation for a stochastic game, and the vanishing discount method is used to study its solution. In addition, we prove that the existence conditions are as well necessary.en_US
dc.format.extent188385 bytes
dc.relation.ispartofseriesISR; TR 1996-9en_US
dc.subjectrobust controlen_US
dc.subjectstochastic systemsen_US
dc.subjectintelligent servo: stochasticen_US
dc.subjectintelligent servo: risk sensitive controlen_US
dc.subjectstochastic dynamic gamesen_US
dc.subjectIsaac equationen_US
dc.subjectstochastic controlen_US
dc.subjectaverage costen_US
dc.subjectSystems Integration Methodologyen_US
dc.titleRisk Sensitive Control of Markov Processes in Countable State Spaceen_US
dc.typeTechnical Reporten_US

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