A Multi-Layer Deep Reinforcement Learning Approach for Joint Task Offloading and Scheduling in Vehicular Edge Networks-Reference-Cited by-同舟云学术

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A Multi-Layer Deep Reinforcement Learning Approach for Joint Task Offloading and Scheduling in Vehicular Edge Networks

Published:2023-05-28 Issue: Volume: Page:
ISSN:
Container-title:ICC 2023 - IEEE International Conference on Communications
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Author:

Wu Jiaqi¹,Ye Ziyuan¹,He Lin¹,Wang Tong¹,Gao Lin²

Affiliation:

1. The School of Electronics and Information Engineering, Harbin Institute of Technology,Guangdong Provincial Key Laboratory of Aerospace Communication and Networking Technology,Shenzhen,China

2. The Shenzhen Institute of Artificial Intelligence and Robotics for Society,Shenzhen,China

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10278505/10278554/10279516.pdf?arnumber=10279516

Reference14 articles.

1. Collaborative Data Scheduling for Vehicular Edge Computing via Deep Reinforcement Learning

2. Spectrum Sharing in Vehicular Networks Based on Multi-Agent Reinforcement Learning

3. Deep Reinforcement Learning for Online Computation Offloading in Wireless Powered Mobile-Edge Computing Networks

4. DRL-Based V2V Computation Offloading for Blockchain-Enabled Vehicular Networks;shi;IEEE Transactions on Mobile Computing,2022

5. A Deep Reinforcement Learning Approach for Collaborative Mobile Edge Computing

Cited by 1 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

1. Multi-UAV Cooperative Search in Multi-Layered Aerial Computing Networks: A Multi-Agent Deep Reinforcement Learning Approach;ICC 2024 - IEEE International Conference on Communications;2024-06-09

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