Approximate Receding Horizon Approach for Markov Decision Processes: Average Reward Case

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dc.contributor.authorChang, Hyeong Sooen_US
dc.contributor.authorMarcus, Steven I.en_US
dc.contributor.departmentISRen_US
dc.date.accessioned2007-05-23T10:10:51Z
dc.date.available2007-05-23T10:10:51Z
dc.date.issued2001en_US
dc.description.abstractBuilding on the receding horizon approach by Hernandez-Lerma andLasserre in solving Markov decision processes (MDPs),this paper first analyzes the performance of the (approximate) receding horizon approach in terms of infinite horizon average reward. <p>In this approach, we fix a finite horizon and at each decision time, we solve the given MDP with the finite horizon for an approximately optimal current action and take the action to control the MDP.<p>We then analyze recently proposed on-line policy improvementscheme, "roll-out," by Bertsekas and Castanon, and a generalization of the rollout algorithm, "parallel rollout" by Chang et al., in terms of the infinite horizon average reward in the framework of the (approximate) receding horizon control.<p>We finally discuss practical implementations of these schemes via simulation.en_US
dc.format.extent188249 bytes
dc.format.mimetypeapplication/pdf
dc.identifier.urihttp://hdl.handle.net/1903/6208
dc.language.isoen_USen_US
dc.relation.ispartofseriesISR; TR 2001-46en_US
dc.subjectGlobal Communication Systemsen_US
dc.titleApproximate Receding Horizon Approach for Markov Decision Processes: Average Reward Caseen_US
dc.typeTechnical Reporten_US

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