Deep Reinforcement Learning Method for Task Offloading in Mobile Edge Computing Networks Based on Parallel Exploration with Asynchronous Training-Reference-Cited by-同舟云学术

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Deep Reinforcement Learning Method for Task Offloading in Mobile Edge Computing Networks Based on Parallel Exploration with Asynchronous Training

Published:2024-08-28 Issue: Volume: Page:
ISSN:1383-469X
Container-title:Mobile Networks and Applications
language:en
Short-container-title:Mobile Netw Appl

Author:

Chen Junyan,Jin Lei,Yao Rui,Zhang Hongmei

Funder

National Natural Science Foundation of China

Major program of Guangxi Natural Science Foundation

Middle-aged and Young Teachers' Basic Ability Promotion Project of Guangxi

Innovation Project of GUET Graduate Education

Publisher

Springer Science and Business Media LLC

Link

https://link.springer.com/content/pdf/10.1007/s11036-024-02397-7.pdf

Reference44 articles.

1. Mao Y, You C, Zhang J et al (2017) A survey on mobile edge computing: The communication perspective. IEEE Commun Surveys Tuts 19(4):2322–2358

2. Xie R, Lian X, Jia Q et al (2018) Survey on computation offloading in mobile edge computing. J Commun 39(11):138–155

3. Chen Z, Zhang J, Huang Z et al (2024) Computation offloading in blockchain-enabled MCS systems: A scalable deep reinforcement learning approach. Futur Gener Comput Syst 53:301–311

4. Chen Z, Hu J, Min G et al (2022) Adaptive and efficient resource allocation in cloud datacenters using actor-critic deep reinforcement learning. IEEE Trans Parallel Distrib Syst 33(8):1911–1923

5. Liu J, Wu Z, Liu J et al (2022) Cost research of internet of things service architecture for random mobile users based on edge computing. Int J Web Inf Syst 4(18):217–235

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