1. ZHAW digitalcollection
  2. School of Engineering
  3. Publikationen
  4. Publikationen School of Engineering
Please use this identifier to cite or link to this item: https://doi.org/10.21256/zhaw-19978
Full metadata record
DC FieldValueLanguage
dc.contributor.authorRoost, Dano-
dc.contributor.authorMeier, Ralph-
dc.contributor.authorHuschauer, Stephan-
dc.contributor.authorNygren, Erik-
dc.contributor.authorEgli, Adrian-
dc.contributor.authorWeiler, Andreas-
dc.contributor.authorStadelmann, Thilo-
dc.date.accessioned2020-04-30T09:02:01Z-
dc.date.available2020-04-30T09:02:01Z-
dc.date.issued2020-06-26-
dc.identifier.urihttps://digitalcollection.zhaw.ch/handle/11475/19978-
dc.description.abstractWe present preliminary results from our sixth placed entry to the Flatland international competition for train rescheduling, including two improvements for optimized reinforcement learning (RL) training efficiency, and two hypotheses with respect to the prospect of deep RL for complex real-world control tasks: first, that current state of the art policy gradient methods seem inappropriate in the domain of high-consequence environments; second, that learning explicit communication actions (an emerging machine-to-machine language, so to speak) might offer a remedy. These hypotheses need to be confirmed by future work. If confirmed, they hold promises with respect to optimizing highly efficient logistics ecosystems like the Swiss Federal Railways railway network.de_CH
dc.language.isoende_CH
dc.publisherIEEEde_CH
dc.rightsLicence according to publishing contractde_CH
dc.subjectMulti-agent deep reinforcement learningde_CH
dc.subject.ddc006: Spezielle Computerverfahrende_CH
dc.titleImproving sample efficiency and multi-agent communication in RL-based train reschedulingde_CH
dc.typeKonferenz: Paperde_CH
dcterms.typeTextde_CH
zhaw.departementSchool of Engineeringde_CH
zhaw.organisationalunitInstitut für Informatik (InIT)de_CH
dc.identifier.doi10.21256/zhaw-19978-
zhaw.conference.details7th Swiss Conference on Data Science, Lucerne, Switzerland, 26 June 2020de_CH
zhaw.funding.euNode_CH
zhaw.originated.zhawYesde_CH
zhaw.publication.statusacceptedVersionde_CH
zhaw.publication.reviewPeer review (Publikation)de_CH
zhaw.title.proceedingsProceedings of the 7th SDSde_CH
zhaw.webfeedDatalabde_CH
zhaw.webfeedInformation Engineeringde_CH
zhaw.webfeedZHAW digitalde_CH
zhaw.webfeedMachine Perception and Cognitionde_CH
zhaw.author.additionalNode_CH
zhaw.display.portraitYesde_CH
Appears in collections:Publikationen School of Engineering

Files in This Item:
File Description SizeFormat 
2020_Roost-etal_RL-based-train-rescheduling_SDS2020.pdfAccepted Version347.87 kBAdobe PDFThumbnail
View/Open
Show simple item record
Roost, D., Meier, R., Huschauer, S., Nygren, E., Egli, A., Weiler, A., & Stadelmann, T. (2020, June 26). Improving sample efficiency and multi-agent communication in RL-based train rescheduling. Proceedings of the 7th SDS. https://doi.org/10.21256/zhaw-19978
Roost, D. et al. (2020) ‘Improving sample efficiency and multi-agent communication in RL-based train rescheduling’, in Proceedings of the 7th SDS. IEEE. Available at: https://doi.org/10.21256/zhaw-19978.
D. Roost et al., “Improving sample efficiency and multi-agent communication in RL-based train rescheduling,” in Proceedings of the 7th SDS, Jun. 2020. doi: 10.21256/zhaw-19978.
ROOST, Dano, Ralph MEIER, Stephan HUSCHAUER, Erik NYGREN, Adrian EGLI, Andreas WEILER und Thilo STADELMANN, 2020. Improving sample efficiency and multi-agent communication in RL-based train rescheduling. In: Proceedings of the 7th SDS. Conference paper. IEEE. 26 Juni 2020
Roost, Dano, Ralph Meier, Stephan Huschauer, Erik Nygren, Adrian Egli, Andreas Weiler, and Thilo Stadelmann. 2020. “Improving Sample Efficiency and Multi-Agent Communication in RL-Based Train Rescheduling.” Conference paper. In Proceedings of the 7th SDS. IEEE. https://doi.org/10.21256/zhaw-19978.
Roost, Dano, et al. “Improving Sample Efficiency and Multi-Agent Communication in RL-Based Train Rescheduling.” Proceedings of the 7th SDS, IEEE, 2020, https://doi.org/10.21256/zhaw-19978.


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.