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Abstract:
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.
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Source :
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
ISSN: 2162-237X
Year: 2022
Issue: 10
Volume: 34
Page: 7430-7442
1 0 . 4
JCR@2022
1 0 . 4 0 0
JCR@2022
ESI Discipline: COMPUTER SCIENCE;
ESI HC Threshold:46
JCR Journal Grade:1
CAS Journal Grade:1
Cited Count:
WoS CC Cited Count: 38
SCOPUS Cited Count: 44
ESI Highly Cited Papers on the List: 0 Unfold All
WanFang Cited Count:
Chinese Cited Count:
30 Days PV: 5
Affiliated Colleges: