Reinforced optimal control
dc.bibliographicCitation.seriesTitle | WIAS Preprints | eng |
dc.bibliographicCitation.volume | 2792 | |
dc.contributor.author | Bayer, Christian | |
dc.contributor.author | Belomestny, Denis | |
dc.contributor.author | Hager, Paul | |
dc.contributor.author | Pigato, Paolo | |
dc.contributor.author | Schoenmakers, John G. M. | |
dc.contributor.author | Spokoiny, Vladimir | |
dc.date.accessioned | 2022-06-30T13:24:03Z | |
dc.date.available | 2022-06-30T13:24:03Z | |
dc.date.issued | 2020 | |
dc.description.abstract | Least squares Monte Carlo methods are a popular numerical approximation method for solving stochastic control problems. Based on dynamic programming, their key feature is the approximation of the conditional expectation of future rewards by linear least squares regression. Hence, the choice of basis functions is crucial for the accuracy of the method. Earlier work by some of us [Belomestny, Schoenmakers, Spokoiny, Zharkynbay, Commun. Math. Sci., 18(1):109?121, 2020] proposes to reinforce the basis functions in the case of optimal stopping problems by already computed value functions for later times, thereby considerably improving the accuracy with limited additional computational cost. We extend the reinforced regression method to a general class of stochastic control problems, while considerably improving the method?s efficiency, as demonstrated by substantial numerical examples as well as theoretical analysis. | eng |
dc.description.version | publishedVersion | eng |
dc.identifier.uri | https://oa.tib.eu/renate/handle/123456789/9442 | |
dc.identifier.uri | https://doi.org/10.34657/8480 | |
dc.language.iso | eng | |
dc.publisher | Berlin : Weierstraß-Institut für Angewandte Analysis und Stochastik | |
dc.relation.doi | https://doi.org/10.20347/WIAS.PREPRINT.2792 | |
dc.relation.issn | 2198-5855 | |
dc.rights.license | This document may be downloaded, read, stored and printed for your own use within the limits of § 53 UrhG but it may not be distributed via the internet or passed on to external parties. | eng |
dc.rights.license | Dieses Dokument darf im Rahmen von § 53 UrhG zum eigenen Gebrauch kostenfrei heruntergeladen, gelesen, gespeichert und ausgedruckt, aber nicht im Internet bereitgestellt oder an Außenstehende weitergegeben werden. | ger |
dc.subject.ddc | 510 | |
dc.subject.other | Reinforced regression | eng |
dc.subject.other | least squares Monte Carlo | eng |
dc.subject.other | stochastic optimal control | eng |
dc.title | Reinforced optimal control | eng |
dc.type | Report | eng |
dc.type | Text | eng |
dcterms.extent | 30 S. | |
tib.accessRights | openAccess | |
wgl.contributor | WIAS | |
wgl.subject | Mathematik | |
wgl.type | Report / Forschungsbericht / Arbeitspapier |
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