Deep Reinforcement Learning Approach for UAV-Assisted Mobile Edge Computing Networks-Reference-Cited by-同舟云学术

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Deep Reinforcement Learning Approach for UAV-Assisted Mobile Edge Computing Networks

Published:2022-12-04 Issue: Volume: Page:
ISSN:
Container-title:GLOBECOM 2022 - 2022 IEEE Global Communications Conference
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Author:

Hwang Sangwon¹,Park Juseong²,Lee Hoon³,Kim Mintae¹,Lee Inkyu¹

Affiliation:

1. School of Electrical Engineering, Korea University,Seoul,Korea

2. The University of Texas at Austin,Department of Electrical and Computer Engineering,Austin,TX,USA

3. Pukyoung National University,Department of Information and Communications Engineering,Busan,Korea

Funder

National Research Foundation of Korea

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10000063/10000593/10001011.pdf?arnumber=10001011

Reference20 articles.

1. Continuous control with deep reinforcement learning;lillicrap;Proc Int Conf on Learning Representations (ICLR),0

2. Human-level control through deep reinforcement learning;mnih;Nature,2015

3. Unmanned Aerial Vehicle With Underlaid Device-to-Device Communications: Performance and Tradeoffs

4. Addressing function approximation error in actor-critic methods;fujimoto;Proc Int Conf on Machine Learning (ICML),0

5. Mobility Models, Traces and Impact of Mobility on Opportunistic Routing Algorithms: A Survey

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3. Decentralized Learning Framework for Hierarchical Wireless Networks: A Tree Neural Network Approach;IEEE Internet of Things Journal;2024-05-15

4. Task offloading and trajectory scheduling for UAV-enabled MEC networks: An MADRL algorithm with prioritized experience replay;Ad Hoc Networks;2024-03

5. Intelligent UAV planning for task-offloading with limited buffer and multiple computing servers;Physical Communication;2024-02

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