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Reinforcement Learning Control for Discrete-time Systems over Fading Channels

被引：0
|
作者：
Lai, Jing [1 ]
Xiong, Junlin [1 ]
机构：
[1] Univ Sci & Technol China, Hefei 230026, Peoples R China
来源：
IFAC PAPERSONLINE | 2023年 / 56卷 / 02期
基金：
中国国家自然科学基金;
关键词：
fading channels; reinforcement learning; generalized policy iteration; GENERALIZED POLICY ITERATION;
D O I：
10.1016/j.ifacol.2023.10.1251
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
This paper studies the average cost minimization problem for a class of discrete-time systems suffering from fading channels and additive noise. An approximate generalized policy iteration algorithm is proposed to search for the optimal control policy for the considered problem without resorting to the system matrices. The estimation errors of the kernel matrices are proven to be small and the proposed model-free reinforcement learning algorithm is proven to be convergent. Some numerical examples are provided to illustrate the obtained results. Copyright (c) 2023 The Authors.
引用
收藏
页码：4834 / 4839
页数：6
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