Title: Comprehensive Comparison of Online ADP Algorithms for Continuous-Time Optimal Control
Authors: Zhu, YH; Zhao, DB
Author Full Names: Zhu, Yuanheng; Zhao, Dongbin
Source: ARTIFICIAL INTELLIGENCE REVIEW, 49 (4):531-547; 10.1007/s10462-017-9548-4 APR 2018
Abstract: Online learning is an important property of adaptive dynamic programming (ADP). Online observations contain plentiful dynamics information, and ADP algorithms can utilize them to learn the optimal control policy. This paper reviews the research of online ADP algorithms for the optimal control of continuous-time systems. With the intensive study, ADP has been developed towards model free and data efficient. After separately introducing the algorithms, we compare their performance on the same problem. This paper is desired to provide a comprehensive understanding of continuous-time online ADP algorithms.
IDS Number: FY5XV
Unique ID: WOS:000426912500004