Eich, Yannick ; Alt, Bastian ; Koeppl, Heinz (2024)
Approximate Control for Continuous-Time POMDPs.
27th International Conference on Artificial Intelligence and Statistics. Valencia, Spain (02.05.2024 - 04.05.2024)
Konferenzveröffentlichung, Bibliographie
Dies ist die neueste Version dieses Eintrags.
Kurzbeschreibung (Abstract)
This work proposes a decision-making framework for partially observable systems in continuous time with discrete state and action spaces. As optimal decision-making becomes intractable for large state spaces we employ approximation methods for the filtering and the control problem that scale well with an increasing number of states. Specifically, we approximate the high-dimensional filtering distribution by projecting it onto a parametric family of distributions, and integrate it into a control heuristic based on the fully observable system to obtain a scalable policy. We demonstrate the effectiveness of our approach on several partially observed systems, including queueing systems and chemical reaction networks.
| Typ des Eintrags: | Konferenzveröffentlichung |
|---|---|
| Erschienen: | 2024 |
| Autor(en): | Eich, Yannick ; Alt, Bastian ; Koeppl, Heinz |
| Art des Eintrags: | Bibliographie |
| Titel: | Approximate Control for Continuous-Time POMDPs |
| Sprache: | Englisch |
| Publikationsjahr: | 2024 |
| Verlag: | PMLR |
| Buchtitel: | Proceedings of The 27th International Conference on Artificial Intelligence and Statistics |
| Reihe: | Proceedings of Machine Learning Research |
| Band einer Reihe: | 238 |
| Veranstaltungstitel: | 27th International Conference on Artificial Intelligence and Statistics |
| Veranstaltungsort: | Valencia, Spain |
| Veranstaltungsdatum: | 02.05.2024 - 04.05.2024 |
| Zugehörige Links: | |
| Kurzbeschreibung (Abstract): | This work proposes a decision-making framework for partially observable systems in continuous time with discrete state and action spaces. As optimal decision-making becomes intractable for large state spaces we employ approximation methods for the filtering and the control problem that scale well with an increasing number of states. Specifically, we approximate the high-dimensional filtering distribution by projecting it onto a parametric family of distributions, and integrate it into a control heuristic based on the fully observable system to obtain a scalable policy. We demonstrate the effectiveness of our approach on several partially observed systems, including queueing systems and chemical reaction networks. |
| Zusätzliche Informationen: | Erstveröffentlichung |
| Sachgruppe der Dewey Dezimalklassifikatin (DDC): | 000 Allgemeines, Informatik, Informationswissenschaft > 004 Informatik 600 Technik, Medizin, angewandte Wissenschaften > 621.3 Elektrotechnik, Elektronik |
| Fachbereich(e)/-gebiet(e): | 18 Fachbereich Elektrotechnik und Informationstechnik 18 Fachbereich Elektrotechnik und Informationstechnik > Institut für Nachrichtentechnik > Bioinspirierte Kommunikationssysteme 18 Fachbereich Elektrotechnik und Informationstechnik > Institut für Nachrichtentechnik 18 Fachbereich Elektrotechnik und Informationstechnik > Self-Organizing Systems Lab |
| Hinterlegungsdatum: | 28 Nov 2024 08:53 |
| Letzte Änderung: | 28 Nov 2024 08:53 |
| PPN: | |
| Export: | |
| Suche nach Titel in: | TUfind oder in Google |
Verfügbare Versionen dieses Eintrags
-
Approximate Control for Continuous-Time POMDPs. (deposited 25 Nov 2024 10:47)
- Approximate Control for Continuous-Time POMDPs. (deposited 28 Nov 2024 08:53) [Gegenwärtig angezeigt]
 |
Frage zum Eintrag |
Optionen (nur für Redakteure)
 |
Redaktionelle Details anzeigen |
Drucken
Impressum