Data-driven approximate dynamic programming : A linear programming approach

Lade...
Vorschaubild
Dateien
Zu diesem Dokument gibt es keine Dateien.
Datum
2017
Autor:innen
Kamoutsi, Angeliki
Esfahani, Peyman Mohajerin
Lygeros, John
Herausgeber:innen
Kontakt
ISSN der Zeitschrift
Electronic ISSN
ISBN
Bibliografische Daten
Verlag
Schriftenreihe
Auflagebezeichnung
URI (zitierfähiger Link)
ArXiv-ID
Internationale Patentnummer
EU-Projektnummer
DFG-Projektnummer
Angaben zur Forschungsförderung (Freitext)
Projekt
Open Access-Veröffentlichung
Gesperrt bis
Titel in einer weiteren Sprache
Forschungsvorhaben
Organisationseinheiten
Zeitschriftenheft
Publikationstyp
Beitrag zu einem Konferenzband
Publikationsstatus
Published
Erschienen in
2017 IEEE 56th Annual Conference on Decision and Control (CDC). Piscataway, NJ: IEEE, 2017, pp. 5174-5179. ISBN 978-1-5090-2873-3. Available under: doi: 10.1109/CDC.2017.8264426
Zusammenfassung

This article presents an approximation scheme for the infinite-dimensional linear programming formulation of discrete-time Markov control processes via a finite-dimensional convex program, when the dynamics are unknown and learned from data. We derive a probabilistic explicit error bound between the data-driven finite convex program and the original infinite linear program. We further discuss the sample complexity of the error bound which translates to the number of samples required for an a priori approximation accuracy. Our analysis sheds light on the impact of the choice of basis functions for approximating the true value function. Finally, the relevance of the method is illustrated on a truncated LQG problem.

Zusammenfassung in einer weiteren Sprache
Fachgebiet (DDC)
004 Informatik
Schlagwörter
Konferenz
IEEE 56th Annual Conference on Decision and Control (CDC), 12. Dez. 2017 - 15. Dez. 2017, Melbourne, Australia
Rezension
undefined / . - undefined, undefined
Zitieren
ISO 690SUTTER, Tobias, Angeliki KAMOUTSI, Peyman Mohajerin ESFAHANI, John LYGEROS, 2017. Data-driven approximate dynamic programming : A linear programming approach. IEEE 56th Annual Conference on Decision and Control (CDC). Melbourne, Australia, 12. Dez. 2017 - 15. Dez. 2017. In: 2017 IEEE 56th Annual Conference on Decision and Control (CDC). Piscataway, NJ: IEEE, 2017, pp. 5174-5179. ISBN 978-1-5090-2873-3. Available under: doi: 10.1109/CDC.2017.8264426
BibTex
@inproceedings{Sutter2017Datad-55738,
  year={2017},
  doi={10.1109/CDC.2017.8264426},
  title={Data-driven approximate dynamic programming : A linear programming approach},
  isbn={978-1-5090-2873-3},
  publisher={IEEE},
  address={Piscataway, NJ},
  booktitle={2017 IEEE 56th Annual Conference on Decision and Control (CDC)},
  pages={5174--5179},
  author={Sutter, Tobias and Kamoutsi, Angeliki and Esfahani, Peyman Mohajerin and Lygeros, John}
}
RDF
<rdf:RDF
    xmlns:dcterms="http://purl.org/dc/terms/"
    xmlns:dc="http://purl.org/dc/elements/1.1/"
    xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#"
    xmlns:bibo="http://purl.org/ontology/bibo/"
    xmlns:dspace="http://digital-repositories.org/ontologies/dspace/0.1.0#"
    xmlns:foaf="http://xmlns.com/foaf/0.1/"
    xmlns:void="http://rdfs.org/ns/void#"
    xmlns:xsd="http://www.w3.org/2001/XMLSchema#" > 
  <rdf:Description rdf:about="https://kops.uni-konstanz.de/server/rdf/resource/123456789/55738">
    <dc:contributor>Sutter, Tobias</dc:contributor>
    <dspace:isPartOfCollection rdf:resource="https://kops.uni-konstanz.de/server/rdf/resource/123456789/36"/>
    <dc:contributor>Esfahani, Peyman Mohajerin</dc:contributor>
    <dcterms:available rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2021-12-02T12:46:29Z</dcterms:available>
    <dcterms:isPartOf rdf:resource="https://kops.uni-konstanz.de/server/rdf/resource/123456789/36"/>
    <dcterms:rights rdf:resource="https://rightsstatements.org/page/InC/1.0/"/>
    <dc:creator>Lygeros, John</dc:creator>
    <bibo:uri rdf:resource="https://kops.uni-konstanz.de/handle/123456789/55738"/>
    <dc:creator>Kamoutsi, Angeliki</dc:creator>
    <dc:creator>Esfahani, Peyman Mohajerin</dc:creator>
    <dc:date rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2021-12-02T12:46:29Z</dc:date>
    <dc:language>eng</dc:language>
    <dcterms:issued>2017</dcterms:issued>
    <foaf:homepage rdf:resource="http://localhost:8080/"/>
    <dc:creator>Sutter, Tobias</dc:creator>
    <dc:rights>terms-of-use</dc:rights>
    <void:sparqlEndpoint rdf:resource="http://localhost/fuseki/dspace/sparql"/>
    <dcterms:abstract xml:lang="eng">This article presents an approximation scheme for the infinite-dimensional linear programming formulation of discrete-time Markov control processes via a finite-dimensional convex program, when the dynamics are unknown and learned from data. We derive a probabilistic explicit error bound between the data-driven finite convex program and the original infinite linear program. We further discuss the sample complexity of the error bound which translates to the number of samples required for an a priori approximation accuracy. Our analysis sheds light on the impact of the choice of basis functions for approximating the true value function. Finally, the relevance of the method is illustrated on a truncated LQG problem.</dcterms:abstract>
    <dc:contributor>Lygeros, John</dc:contributor>
    <dc:contributor>Kamoutsi, Angeliki</dc:contributor>
    <dcterms:title>Data-driven approximate dynamic programming : A linear programming approach</dcterms:title>
  </rdf:Description>
</rdf:RDF>
Interner Vermerk
xmlui.Submission.submit.DescribeStep.inputForms.label.kops_note_fromSubmitter
Kontakt
URL der Originalveröffentl.
Prüfdatum der URL
Prüfungsdatum der Dissertation
Finanzierungsart
Kommentar zur Publikation
Allianzlizenz
Corresponding Authors der Uni Konstanz vorhanden
Internationale Co-Autor:innen
Universitätsbibliographie
Nein
Begutachtet
Diese Publikation teilen