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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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Автор: Puccetti, Luca
Формат: Online
Мова:Англійська
Опубліковано: KIT Scientific Publishing 2023
Предмети:
Regelungstechnik; Künstliche Intelligenz; Fahrzeugregelung; Längsdynamik; Bestärkendes Lernen; Control Theory; Artificial Intelligence; Vehicle Control; Longitudinal Dynamics; Reinforcement Learning
thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TH Energy technology and engineering::THR Electrical engineering
Онлайн доступ:https://library.oapen.org/handle/20.500.12657/63614
Теги: Додати тег
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Резюме: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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