Cooperative Task Offloading and Service Caching for Digital Twin Edge Networks: A Graph Attention Multi-Agent Reinforcement Learning Approach
Author:
Affiliation:
1. School of Communications and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing, China
2. School of Software Engineering, Chongqing University of Posts and Telecommunications, Chongqing, China
Funder
National Natural Science Foundation of China
China Postdoctoral Science Foundation
Natural Science Foundation of Chongqing, China
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
Electrical and Electronic Engineering,Computer Networks and Communications
Link
http://xplorestaging.ieee.org/ielx7/49/10297955/10235999.pdf?arnumber=10235999
Reference45 articles.
1. Multi-Agent Reinforcement Learning Based Resource Management in MEC- and UAV-Assisted Vehicular Networks
2. Computation Offloading and Service Caching for Intelligent Transportation Systems With Digital Twin
3. Adaptive Digital Twin and Multiagent Deep Reinforcement Learning for Vehicular Edge Computing and Networks
4. URLLC Edge Networks With Joint Optimal User Association, Task Offloading and Resource Allocation: A Digital Twin Approach
5. A Multi-Agent Deep Reinforcement Learning Approach for Computation Offloading in 5G Mobile Edge Computing
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4. Graph Neural Network Based Asynchronous Federated Learning for Digital Twin-Driven Distributed Multi-Agent Dynamical Systems;Mathematics;2024-08-09
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