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Self-Learning Longitudinal Control for On-Road Vehicles

Self-Learning Longitudinal Control for On-Road Vehicles

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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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DOI: 10.5445/KSP/1000156966

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