A Proactive Eavesdropping Game in MIMO Systems Based on Multiagent Deep Reinforcement Learning-Reference-Cited by-同舟云学术

A Proactive Eavesdropping Game in MIMO Systems Based on Multiagent Deep Reinforcement Learning

Author:

Guo Delin¹^ORCID,Ding Hui²,Tang Lan¹^ORCID,Zhang Xinggan¹^ORCID,Yang Lvxi³^ORCID,Liang Ying-Chang⁴^ORCID

Affiliation:

1. School of Electronic Science and Engineering, Nanjing University, Nanjing, China

2. State Key Laboratory of Air Traffic Management Systems and Technology, Nanjing, China

3. School of Information Science and Engineering, Southeast University, Nanjing, China

4. Center for Intelligent Networking and Communications (CINC), University of Electronic Science and Technology of China (UESTC), Chengdu, China

Funder

National Natural Science Foundation of China

State Key Laboratory of Air Traffic Management System and Technology

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Applied Mathematics,Electrical and Electronic Engineering,Computer Science Applications

Link

Reference61 articles.

3. Deep reinforcement learning from self-play in imperfect-information games;heinrich;arXiv 1603 01121,2016

4. Nash Q-learning for general-sum stochastic games;hu;J Mach Learn Res,2003

Cited by 12 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

2. A Survey of Deep Reinforcement Learning in Game Playing;2024 MIT Art, Design and Technology School of Computing International Conference (MITADTSoCiCon);2024-04-25