收录:
摘要:
In this article, a novel value iteration scheme is developed with convergence and stability discussions. A relaxation factor is introduced to adjust the convergence rate of the value function sequence. The convergence conditions with respect to the relaxation factor are given. The stability of the closed-loop system using the control policies generated by the present VI algorithm is investigated. Moreover, an integrated VI approach is developed to accelerate and guarantee the convergence by combining the advantages of the present and traditional value iterations. Also, a relaxation function is designed to adaptively make the developed value iteration scheme possess fast convergence property. Finally, the theoretical results and the effectiveness of the present algorithm are validated by numerical examples.
关键词:
通讯作者信息:
来源 :
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
ISSN: 2162-237X
年份: 2022
期: 10
卷: 34
页码: 7430-7442
1 0 . 4
JCR@2022
1 0 . 4 0 0
JCR@2022
ESI学科: COMPUTER SCIENCE;
ESI高被引阀值:46
JCR分区:1
中科院分区:1
归属院系: