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Reinforcement Learning for Speed Control with Feedforward to Track Velocity Profiles in a Real Vehicle

Köpf, Florian; Puccetti, Luca ORCID iD icon; Rathgeber, Christian; Hohmann, Sören

Abstract (englisch):

Reinforcement learning (RL) for tracking arbitrary trajectories in real control applications with unobserved dynamics is a challenging subject of current research. An example of such an application is given by the speed control of a real car with unobserved longitudinal dynamics. We propose an online RL method, where the RL agent explicitly incorporates information concerning the desired velocity profile by means of a time-varying first order trajectory approximation. Furthermore, missing state information is reconstructed by learning a finite impulse response filter. We apply the presented algorithm in online training of controller parameters in a real car. Our validation results reveal that our proposed method clearly outperforms a controller that uses set points of the desired velocity by means of the resulting velocity tracking performance.

DOI: 10.1109/ITSC45102.2020.9294541
Zitationen: 1
Zugehörige Institution(en) am KIT Institut für Regelungs- und Steuerungssysteme (IRS)
Publikationstyp Proceedingsbeitrag
Publikationsjahr 2020
Sprache Englisch
Identifikator ISBN: 978-1-72814-149-7
KITopen-ID: 1000122267
Erschienen in 23rd IEEE International Conference on Intelligent Transportation Systems (ITSC), September 20 – 23, 2020
Veranstaltung 23rd International Conference on Intelligent Transportation Systems (ITSC 2020), Online, 20.09.2020 – 23.09.2020
Verlag Institute of Electrical and Electronics Engineers (IEEE)
Seiten Art.Nr. 9294541
Bemerkung zur Veröffentlichung Gesamtwerk DOI: 10.1109/ITSC45102.2020
Schlagwörter Reinforcement Learning, Actor-Critic Learning, Velocity Controller
Nachgewiesen in Dimensions
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