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01667nma a2200301 u 4500 |
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EBX01000000000000001311362 |
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00000000000000.0 |
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230811 ||| eng |
| 020 |
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|a 9783731512905
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|a 1000156966
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1 |
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|a Puccetti, Luca
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| 245 |
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0 |
|a Self-Learning Longitudinal Control for On-Road Vehicles
|h Elektronische Ressource
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| 260 |
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|b KIT Scientific Publishing
|c 2023
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| 300 |
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|a 1 electronic resource (158 p.)
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| 653 |
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|a Regelungstechnik; Künstliche Intelligenz; Fahrzeugregelung; Längsdynamik; Bestärkendes Lernen; Control Theory; Artificial Intelligence; Vehicle Control; Longitudinal Dynamics; Reinforcement Learning
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| 653 |
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|a Electrical engineering / bicssc
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| 041 |
0 |
7 |
|a eng
|2 ISO 639-2
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| 989 |
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|b DOAB
|a Directory of Open Access Books
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| 490 |
0 |
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|a Karlsruher Beiträge zur Regelungs- und Steuerungstechnik
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| 500 |
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|a Creative Commons (cc), https://creativecommons.org/licenses/by/4.0/
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| 028 |
5 |
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|a 10.5445/KSP/1000156966
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| 856 |
4 |
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|u https://library.oapen.org/bitstream/20.500.12657/63614/1/self-learning-longitudinal-control-for-on-road-vehicles.pdf
|7 0
|x Verlag
|3 Volltext
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| 856 |
4 |
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|u https://directory.doabooks.org/handle/20.500.12854/101654
|z DOAB: description of the publication
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| 082 |
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|a 700
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| 082 |
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|a 620
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| 520 |
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|a 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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