Nass, David ; Belousov, Boris ; Peters, Jan (2022)
Entropic Risk Measure in Policy Search.
International Conference on Intelligent Robots and Systems (IROS). Macau, China (03.-08.11.2019)
Konferenzveröffentlichung, Bibliographie
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Kurzbeschreibung (Abstract)
With the increasing pace of automation, modern robotic systems need to act in stochastic, non-stationary, partially observable environments. A range of algorithms for finding parameterized policies that optimize for long-term average performance have been proposed in the past. However, the majority of the proposed approaches does not explicitly take into account the variability of the performance metric, which may lead to finding policies that although performing well on average, can perform spectacularly bad in a particular run or over a period of time. To address this shortcoming, we study an approach to policy optimization that explicitly takes into account higher order statistics of the reward function. In this paper, we extend policy gradient methods to include the entropic risk measure in the objective function and evaluate their performance in simulation experiments and on a real-robot task of learning a hitting motion in robot badminton.
| Typ des Eintrags: | Konferenzveröffentlichung |
|---|---|
| Erschienen: | 2022 |
| Autor(en): | Nass, David ; Belousov, Boris ; Peters, Jan |
| Art des Eintrags: | Bibliographie |
| Titel: | Entropic Risk Measure in Policy Search |
| Sprache: | Englisch |
| Publikationsjahr: | 2022 |
| Ort: | Darmstadt |
| Verlag: | IEEE |
| Buchtitel: | 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) |
| Kollation: | 6 Seiten |
| Veranstaltungstitel: | International Conference on Intelligent Robots and Systems (IROS) |
| Veranstaltungsort: | Macau, China |
| Veranstaltungsdatum: | 03.-08.11.2019 |
| Zugehörige Links: | |
| Kurzbeschreibung (Abstract): | With the increasing pace of automation, modern robotic systems need to act in stochastic, non-stationary, partially observable environments. A range of algorithms for finding parameterized policies that optimize for long-term average performance have been proposed in the past. However, the majority of the proposed approaches does not explicitly take into account the variability of the performance metric, which may lead to finding policies that although performing well on average, can perform spectacularly bad in a particular run or over a period of time. To address this shortcoming, we study an approach to policy optimization that explicitly takes into account higher order statistics of the reward function. In this paper, we extend policy gradient methods to include the entropic risk measure in the objective function and evaluate their performance in simulation experiments and on a real-robot task of learning a hitting motion in robot badminton. |
| Sachgruppe der Dewey Dezimalklassifikatin (DDC): | 000 Allgemeines, Informatik, Informationswissenschaft > 004 Informatik |
| Fachbereich(e)/-gebiet(e): | 20 Fachbereich Informatik 20 Fachbereich Informatik > Intelligente Autonome Systeme |
| TU-Projekte: | EC/H2020|640554|SKILLS4ROBOTS |
| Hinterlegungsdatum: | 02 Aug 2024 12:45 |
| Letzte Änderung: | 02 Aug 2024 12:45 |
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Entropic Risk Measure in Policy Search. (deposited 22 Nov 2022 09:52)
- Entropic Risk Measure in Policy Search. (deposited 02 Aug 2024 12:45) [Gegenwärtig angezeigt]
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