Nasiri, Mahdi ; Liebchen, Benno (2022)
Reinforcement learning of optimal active particle navigation.
In: New Journal of Physics, 24 (7)
doi: 10.1088/1367-2630/ac8013
Artikel, Bibliographie
Dies ist die neueste Version dieses Eintrags.
Kurzbeschreibung (Abstract)
The development of self-propelled particles at the micro- and the nanoscale has sparked a huge potential for future applications in active matter physics, microsurgery, and targeted drug delivery. However, while the latter applications provoke the quest on how to optimally navigate towards a target, such as e.g. a cancer cell, there is still no simple way known to determine the optimal route in sufficiently complex environments. Here we develop a machine learning-based approach that allows us, for the first time, to determine the asymptotically optimal path of a self-propelled agent which can freely steer in complex environments. Our method hinges on policy gradient-based deep reinforcement learning techniques and, crucially, does not require any reward shaping or heuristics. The presented method provides a powerful alternative to current analytical methods to calculate optimal trajectories and opens a route towards a universal path planner for future intelligent active particles.
| Typ des Eintrags: | Artikel |
|---|---|
| Erschienen: | 2022 |
| Autor(en): | Nasiri, Mahdi ; Liebchen, Benno |
| Art des Eintrags: | Bibliographie |
| Titel: | Reinforcement learning of optimal active particle navigation |
| Sprache: | Englisch |
| Publikationsjahr: | 2022 |
| Ort: | Darmstadt |
| Verlag: | IOP Publishing |
| Titel der Zeitschrift, Zeitung oder Schriftenreihe: | New Journal of Physics |
| Jahrgang/Volume einer Zeitschrift: | 24 |
| (Heft-)Nummer: | 7 |
| Kollation: | 7 Seiten |
| DOI: | 10.1088/1367-2630/ac8013 |
| Zugehörige Links: | |
| Kurzbeschreibung (Abstract): | The development of self-propelled particles at the micro- and the nanoscale has sparked a huge potential for future applications in active matter physics, microsurgery, and targeted drug delivery. However, while the latter applications provoke the quest on how to optimally navigate towards a target, such as e.g. a cancer cell, there is still no simple way known to determine the optimal route in sufficiently complex environments. Here we develop a machine learning-based approach that allows us, for the first time, to determine the asymptotically optimal path of a self-propelled agent which can freely steer in complex environments. Our method hinges on policy gradient-based deep reinforcement learning techniques and, crucially, does not require any reward shaping or heuristics. The presented method provides a powerful alternative to current analytical methods to calculate optimal trajectories and opens a route towards a universal path planner for future intelligent active particles. |
| Freie Schlagworte: | active matter physics, colloids, soft matter physics, microswimmers, optimal navigation, reinforcement learning, optimization |
| Sachgruppe der Dewey Dezimalklassifikatin (DDC): | 500 Naturwissenschaften und Mathematik > 530 Physik |
| Fachbereich(e)/-gebiet(e): | 05 Fachbereich Physik 05 Fachbereich Physik > Institut für Physik Kondensierter Materie (IPKM) |
| Hinterlegungsdatum: | 02 Aug 2024 12:42 |
| Letzte Änderung: | 02 Aug 2024 12:42 |
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| Suche nach Titel in: | TUfind oder in Google |
Verfügbare Versionen dieses Eintrags
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Reinforcement learning of optimal active particle navigation. (deposited 12 Aug 2022 12:06)
- Reinforcement learning of optimal active particle navigation. (deposited 02 Aug 2024 12:42) [Gegenwärtig angezeigt]
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