A Perspective on Deploying Reinforcement Learning to Augment Classic Control Design
Ali Borhan, Cummins
With the advancement in machine learning, access to data with V2X connectivity, and more reliable plant model simulation, reinforcement learning has been considered recently as a control design option for the feedback control of automotive systems. In this talk, the challenges of applying classic control methods with focus on PID structure are briefly discussed and a perspective to deploy reinforcement learning to address some of these challenges is presented.
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