Multi-Agent Reinforcement Learning With Policy Clipping and Average Evaluation for UAV-Assisted Communication Markov Game-Reference-Cited by-同舟云学术

Multi-Agent Reinforcement Learning With Policy Clipping and Average Evaluation for UAV-Assisted Communication Markov Game

Author:

Feng Zikai¹,Huang Mengxing¹^ORCID,Wu Di¹,Wu Edmond Q.²^ORCID,Yuen Chau³^ORCID

Affiliation:

1. School of Information and Communication Engineering, Hainan University, Haikou, China

2. Department of Automation, Shanghai Jiao Tong University, Shanghai, China

3. School of Electrical and Electronic Engineering, Nanyang Technological University, Jurong West, Singapore

Funder

Scientific Research Fund Project of Hainan University

Natural Science Foundation of Hainan Province

Hainan Provincial Natural Science Foundation of China

National Natural Science Foundation of China

State Scholarship Fund, China Scholarship Council

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Computer Science Applications,Mechanical Engineering,Automotive Engineering

Link

Reference44 articles.

2. MARL-based design of multi-unmanned aerial vehicle assisted communication system with hybrid gaming mode;wu;J Electron Inf Technol,2021

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