Joint Caching and Computing Service Placement for Edge-Enabled IoT Based on Deep Reinforcement Learning-Reference-Cited by-同舟云学术

Joint Caching and Computing Service Placement for Edge-Enabled IoT Based on Deep Reinforcement Learning

Author:

Chen Yan¹^ORCID,Sun Yanjing¹^ORCID,Yang Bin²^ORCID,Taleb Tarik³

Affiliation:

1. School of Information and Control Engineering, China University of Mining and Technology, Xuzhou, China

2. School of Computer and Information Engineering, Chuzhou University, Chuzhou, Anhui, China

3. Center of Wireless Communications, University of Oulu, Oulu, Finland

Funder

National Natural Science Foundation of China

Fundamental Research Funds for the Central Universities

Chinese Government Scholarship

Academy of Finland 6Genesis Project

IDEA-MILL

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Computer Networks and Communications,Computer Science Applications,Hardware and Architecture,Information Systems,Signal Processing

Link

Reference39 articles.

1. Multi-agent actor-critic for mixed cooperative-competitive environments;lowe;Proc Neural Inf Process Syst (NIPS),2017

2. Reducing estimation bias via weighted delayed deep deterministic policy gradient;he;arXiv 2006 12622,2020

3. Neural machine translation by jointly learning to align and translate;bahdanau;Proc 3rd Int Conf Learn Represent (ICLR),2015

4. Device placement optimization with reinforcement learning;mirhoseini;Proc 34th Int Conf Mach Learn (PMLR),2017

5. Neural combinatorial optimization with reinforcement learning;bello;arXiv 1611 09940,2016

Cited by 19 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