Guest editorial for special issue on extensions of reinforcement learning and adaptive control

Abstract

Adaptive control is a proven method for learning feedback controllers for systems with unknown dynamic models, exogenous disturbances, nonzero setpoints, and un-modeled nonlinearities. Adaptive control has been applied for years in process control, industry, aerospace systems, vehicle systems, and elsewhere. Reinforcement learning refers to a broad class of methods for improving control policies based on observation of the performance or value of current policies. Reinforcement learning allows the learning of optimal controls in real-time using data measured along system trajectories.