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oapen-20.500.12657-636142023-06-21T04:27:15Z Self-Learning Longitudinal Control for On-Road Vehicles Puccetti, Luca Regelungstechnik; Künstliche Intelligenz; Fahrzeugregelung; Längsdynamik; Bestärkendes Lernen; Control Theory; Artificial Intelligence; Vehicle Control; Longitudinal Dynamics; Reinforcement Learning bic Book Industry Communication::T Technology, engineering, agriculture::TH Energy technology & engineering::THR Electrical engineering Reinforcement Learning is a promising tool to automate controller tuning. However, significant extensions are required for real-world applications to enable fast and robust learning. This work proposes several additions to the state of the art and proves their capability in a series of real world experiments. 2023-06-20T10:55:01Z 2023-06-20T10:55:01Z 2023 book https://library.oapen.org/handle/20.500.12657/63614 eng Karlsruher Beiträge zur Regelungs- und Steuerungstechnik application/pdf Attribution 4.0 International self-learning-longitudinal-control-for-on-road-vehicles.pdf https://doi.org/10.5445/KSP/1000156966 KIT Scientific Publishing 10.5445/KSP/1000156966 10.5445/KSP/1000156966 44e29711-8d53-496b-85cc-3d10c9469be9 20 158 open access
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OAPEN
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English
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description |
Reinforcement Learning is a promising tool to automate controller tuning. However, significant extensions are required for real-world applications to enable fast and robust learning. This work proposes several additions to the state of the art and proves their capability in a series of real world experiments.
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self-learning-longitudinal-control-for-on-road-vehicles.pdf
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spellingShingle |
self-learning-longitudinal-control-for-on-road-vehicles.pdf
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title_short |
self-learning-longitudinal-control-for-on-road-vehicles.pdf
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title_full |
self-learning-longitudinal-control-for-on-road-vehicles.pdf
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title_fullStr |
self-learning-longitudinal-control-for-on-road-vehicles.pdf
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self-learning-longitudinal-control-for-on-road-vehicles.pdf
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self-learning-longitudinal-control-for-on-road-vehicles.pdf
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KIT Scientific Publishing
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2023
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url |
https://doi.org/10.5445/KSP/1000156966
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1771297547441143808
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