Self-Learning Longitudinal Control for On-Road Vehicles
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 ex...
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| Hoofdauteur: | |
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| Formaat: | Elektronisch Hoofdstuk |
| Taal: | Engels |
| Gepubliceerd in: |
KIT Scientific Publishing
2023
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| Reeks: | Karlsruher Beiträge zur Regelungs- und Steuerungstechnik
20 |
| Onderwerpen: | |
| Online toegang: | OAPEN Library: download the publication OAPEN Library: description of the publication |
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| Samenvatting: | 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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| Fysieke beschrijving: | 1 electronic resource (158 p.) |
| ISBN: | KSP/1000156966 |
| Toegang: | Open Access |