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An Asymptotically Efficient Algorithm for Finite Horizon Stochastic Dynamic Programming Problems

dc.contributor.advisorFu, Michael C.en_US
dc.contributor.advisorMarcus, Steven I.en_US
dc.contributor.authorChang, Hyeong Sooen_US
dc.contributor.authorFu, Michael C.en_US
dc.contributor.authorMarcus, Steven I.en_US
dc.date.accessioned2007-05-23T10:13:47Z
dc.date.available2007-05-23T10:13:47Z
dc.date.issued2003en_US
dc.identifier.urihttp://hdl.handle.net/1903/6358
dc.description.abstractWe present a novel algorithm, called ``Simulated Annealing Multiplicative Weights", for approximately solving large finite-horizon stochastic dynamic programming problems. The algorithm is ``asymptotically efficient" in the sense that a finite-time bound for the sample mean of the optimal value function over a given finite policy space can be obtained, and the bound approaches the optimal value as the number of iterations increases.The algorithm updates a probability distribution over the given policy space with a very simple rule, and the sequence of distributions generated by the algorithm converges to a distribution concentrated only on the optimal policies for the given policy space. We also discuss how to reduce the computational cost of the algorithm to apply it in practice.en_US
dc.format.extent259013 bytes
dc.format.mimetypeapplication/pdf
dc.language.isoen_USen_US
dc.relation.ispartofseriesISR; TR 2003-26en_US
dc.subjectNULLen_US
dc.titleAn Asymptotically Efficient Algorithm for Finite Horizon Stochastic Dynamic Programming Problemsen_US
dc.typeTechnical Reporten_US
dc.contributor.departmentISRen_US


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