Enhancing Deep Reinforcement Learning: A Tutorial on Generative Diffusion Models in Network Optimization,IEEE Communications Surveys & Tutorials

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Enhancing Deep Reinforcement Learning: A Tutorial on Generative Diffusion Models in Network Optimization
IEEE Communications Surveys & Tutorials ( IF 34.4 ) Pub Date : 5-10-2024 , DOI: 10.1109/comst.2024.3400011
Hongyang Du ₁ , Ruichen Zhang ₁ , Yinqiu Liu ₁ , Jiacheng Wang ₁ , Yijing Lin ₂ , Zonghang Li ₃ , Dusit Niyato ₁ , Jiawen Kang ₄ , Zehui Xiong ₅ , Shuguang Cui ₆ , Bo Ai ₇ , Haibo Zhou ₈ , Dong In Kim ₉

Affiliation

College of Computing and Data Science, Nanyang Technological University, Singapore, Singapore
State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, China
School of Information and Communication Engineering, University of Electronic Sciences and Technology of China, Chengdu, China
School of Automation, Guangdong University of Technology, China
Pillar of Information Systems Technology and Design, Singapore University of Technology and Design, Singapore, Singapore
School of Science and Engineering (SSE), the Future Network of Intelligence Institute (FNii), and the Guangdong Provincial Key Laboratory of Future Networks of Intelligence, The Chinese University of Hong Kong, Shenzhen, China
State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing, China
School of Electronic Science and Engineering, Nanjing University, Nanjing, Jiangsu, China
Department of Electrical and Computer Engineering, Sungkyunkwan University, Suwon, South Korea