A Deep Reinforcement Learning Based Approach for Intelligent Reconfigurable Surface Elements Selection-Reference-Cited by-同舟云学术

A Deep Reinforcement Learning Based Approach for Intelligent Reconfigurable Surface Elements Selection

Published:2022-09-12 Issue: Volume: Page:
ISSN:
Container-title:2022 IEEE Intl Conf on Dependable, Autonomic and Secure Computing, Intl Conf on Pervasive Intelligence and Computing, Intl Conf on Cloud and Big Data Computing, Intl Conf on Cyber Science and Technology Congress (DASC/PiCom/CBDCom/CyberSciTech)
language:
Short-container-title:

Author:

Zan Shiming¹,Pang Yu¹,Gravina Raffaele²,Cao Enling¹,Li Ye³,Zang Weilin³

Affiliation:

1. Chongqing University of Posts and Telecommunications,School of Optoelectronic Engineering,Chongqing,China

2. University of Calabria,Department of Informatics, Modelling, Electronics and Systems Engineering,Rende,Italy

3. Chinese Academy of Sciences,Shenzhen Institute of Advanced Technology,Shenzhen,China

Funder

National Natural Science Foundation of China

Publisher

IEEE

Link

Reference15 articles.

1. Policy gradient methods for reinforcement learning with function approximation;sutton;Advances in neural information processing systems,1999

2. Continuous control with deep reinforcement learning;lillicrap;Computer ence,2015

3. Densely connected convolutional networks;huang;IEEE Computer Society,2016

Cited by 2 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献