Guided Policy Search Methods: A Review

Abstract

Guided policy search methods (GPSs) have become important methods in the field of reinforcement learning in recent years. GPSs are a kind of policy search methods that utilize trajectory optimization methods to generate training data, guiding supervised learning. In theoretical research, GPSs combine convex optimization and deep learning, and have achieved fruitful results. In practical applications, they have achieved good results in complex control fields such as robots learning, especially manipulator operations. This paper mainly elaborates the development process and improvement route of GPSs. Firstly, the theoretical knowledge related to the GPSs is introduced. Secondly, the framework and basic methods of GPSs are analyzed; Thirdly, various improved GPSs based on the basic methods are generalized. Finally, the development and future improvement directions of GPSs are summarized, and the problems and future development trends are discussed.