Survey of recent progress in data-driven policy optimization for controller design

With the development of communication technology and artificial intelligence, reinforcement learning (RL), as a data-driven control method, has received tremendous attention. The purpose of this survey is to provide an overview of the state-of-the-art policy optimization method for controller design, which is a typical RL method. In particular, we discuss its convergence and sample complexity in certain fundamental optimal control problems in linear systems, such as linear quadratic regulators, output feedback, mathcalH∞control, and distributed control. Additionally, we discuss some future work on the policy optimization for control systems.