Multi-Agent DRL-Based Hungarian Algorithm (MADRLHA) for Task Offloading in Multi-Access Edge Computing Internet of Vehicles (IoVs)
Author:
Affiliation:
1. School of Electrical and Information Engineering, The University of Sydney, Sydney, NSW, Australia
Funder
University of Sydney
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
Applied Mathematics,Electrical and Electronic Engineering,Computer Science Applications
Link
http://xplorestaging.ieee.org/ielx7/7693/9882898/09740530.pdf?arnumber=9740530
Reference38 articles.
1. Deep Reinforcement Learning Based Massive Access Management for Ultra-Reliable Low-Latency Communications
2. Spectrum Sharing in Vehicular Networks Based on Multi-Agent Reinforcement Learning
3. Delay-Sensitive Task Offloading in the 802.11p-Based Vehicular Fog Computing Systems
4. Priority-Aware Task Offloading in Vehicular Fog Computing Based on Deep Reinforcement Learning
5. Parking Edge Computing: Parked-Vehicle-Assisted Task Offloading for Urban VANETs
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