Learning-Based Control: A Tutorial and Some Recent Results (Foundations and Trends® in Systems and Control)

The recent success of Reinforcement Learning and related methods can be attributed to several key factors. First it is driven by reward signals obtained through the interaction with the environment. Second it is closely related to the human learning behavior. Third it has a solid mathematical foundation. Nonetheless conventional Reinforcement Learning theory exhibits some shortcomings particularly in a continuous environment or in considering the stability and robustness of the controlled process.. In this monograph the authors build on Reinforcement Learning to present a learning-based approach for controlling dynamical systems from real-time data and review some major developments in this relatively young field. In doing so the authors develop a framework for learning-based control theory that shows how to learn directly suboptimal controllers from input-output data.. There are three main challenges on the development of learning-based control. First there is a need to generalize existing recursive methods. Second as a fundamental difference between learning-based control and Reinforcement Learning stability and robustness are important issues that must be addressed for the safety-critical engineering systems such as self-driving cars. Third data efficiency of Reinforcement Learning algorithms need be addressed for safety-critical engineering systems.. This monograph provides the reader with an accessible primer on a new direction in control theory still in its infancy namely Learning-Based Control Theory that is closely tied to the literature of safe Reinforcement Learning and Adaptive Dynamic Programming.