DGLR-Publikationsdatenbank - Detailansicht
Autor(en):
M. Thiele, M. Hornung
Zusammenfassung:
A Monte-Carlo Tree Search Optimization Algorithm that is trained using a reinforcement learning approach is enhanced with increased self-learning capabilities. These enable a dynamic and adaptive transition between exploration and exploitation on gained knowledge and a parameter selection process based on probability distribution functions. The algorithm is able to converge to the minimum of commonly used ontimization test functions and can devise an optimal parameter set of a propeller geometry for a fixed-wing VTOL UAV or other design geometric tasks.
Veranstaltung:
Deutscher Luft- und Raumfahrtkongress 2021
Verlag, Ort:
Deutsche Gesellschaft für Luft- und Raumfahrt - Lilienthal-Oberth e.V., Bonn, 2024
Medientyp:
Conference Paper
Sprache:
englisch
Format:
21,0 x 29,7 cm, 13 Seiten
URN:
urn:nbn:de:101:1-2409041312586.801705161714
DOI:
10.25967/550022
Stichworte zum Inhalt:
Reinforcement Learning, Monte-Carlo Tree Search, VTOL UAV, Propeller Design
Verfügbarkeit:
Kommentar:
Zitierform:
Thiele, M.; Hornung, M. (2024): Augmentation of the Self-Guided Learning Capabilities of a Reinforcement Learning based Design Optimization Algorithm for electric Fixed-Wing VTOL UAV Propellers. Deutsche Gesellschaft für Luft- und Raumfahrt - Lilienthal-Oberth e.V.. (Text). https://doi.org/10.25967/550022. urn:nbn:de:101:1-2409041312586.801705161714.
Veröffentlicht am:
04.09.2024