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https://doi.org/10.21256/zhaw-19978| Publikationstyp: | Konferenz: Paper |
| Art der Begutachtung: | Peer review (Publikation) |
| Titel: | Improving sample efficiency and multi-agent communication in RL-based train rescheduling |
| Autor/-in: | Roost, Dano Meier, Ralph Huschauer, Stephan Nygren, Erik Egli, Adrian Weiler, Andreas Stadelmann, Thilo |
| et. al: | No |
| DOI: | 10.21256/zhaw-19978 |
| Tagungsband: | Proceedings of the 7th SDS |
| Angaben zur Konferenz: | 7th Swiss Conference on Data Science, Lucerne, Switzerland, 26 June 2020 |
| Erscheinungsdatum: | 26-Jun-2020 |
| Verlag / Hrsg. Institution: | IEEE |
| Sprache: | Englisch |
| Schlagwörter: | Multi-agent deep reinforcement learning |
| Fachgebiet (DDC): | 006: Spezielle Computerverfahren |
| Zusammenfassung: | We 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. |
| URI: | https://digitalcollection.zhaw.ch/handle/11475/19978 |
| Volltext Version: | Akzeptierte Version |
| Lizenz (gemäss Verlagsvertrag): | Lizenz gemäss Verlagsvertrag |
| Departement: | School of Engineering |
| Organisationseinheit: | Institut für Informatik (InIT) |
| Enthalten in den Sammlungen: | Publikationen School of Engineering |
Dateien zu dieser Ressource:
| Datei | Beschreibung | Größe | Format | |
|---|---|---|---|---|
| 2020_Roost-etal_RL-based-train-rescheduling_SDS2020.pdf | Accepted Version | 347.87 kB | Adobe PDF |  Öffnen/Anzeigen |
Zur Langanzeige
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.
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