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Technology of Network & Communication
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App. Res. of Comp. Printed Article
Vol. 40, 2023 No. 12
3766-3771,3777

Multi-intelligence deep reinforcement learning-based task offloading strategy for disaster emergency scenarios

Mi Dechang
Wang Xiao
Li Mengli
Qin Junkang
School of Electrical Engineering, Guizhou University, Guiyang 550025, China
DOI: 10.19734/j.issn.1001-3695.2023.04.0159

Abstract

For the problems of slow convergence and low utilization of empirical replay groups in traditional DRL, this paper proposed a multi agent deep reinforcement learning(MADRL) based task offloading strategy for disaster emergency scenarios. Firstly, it established a task offloading model based on MADRL for disaster emergency scenarios to deal with the problem of time slot changes in MEC network environment and multi-hop sensor data transmission when disasters occur. Secondly, for the slow convergence problem caused by high-dimensional action space in traditional DRL, it used the mutation and crossover operations of the adaptive differential evolution algorithm(ADE) to explore the action space. And it proposed an adaptive parameter adjustment strategy to adjust the iteration number of ADE, this avoided a large amount of useless exploration of the action space by DRL in the early stages of training. Finally, it added the prioritized experience replay technique to speed up the network training process and improve the data utilization in the experience replay group of DRL. Simulation results show that this adaptive differential evolution algorithm improved deep deterministic policy gradient(ADE-DDPG) saves 35% of the overall overhead compared with the improved original deep deterministic policy gradient(DDPG) network. This verifies the effectiveness of ADE-DDPG in terms of performance.

Foundation Support

国家自然科学基金资助项目(61861007,61640014)
贵州省科技计划资助项目(黔科合基础-ZK［2021］一般303)
贵州省科技支撑计划资助项目(科合支撑［2022］一般017,黔科合支撑［2023］一般096,黔科合支撑［2022］一般264)
贵州省教育厅创新群体项目(黔教合KY字［2021］012)
贵大引进人才项目(贵大人基合字(2014)08号)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2023.04.0159
Publish at: Application Research of Computers Printed Article, Vol. 40, 2023 No. 12
Section: Technology of Network & Communication
Pages: 3766-3771,3777
Serial Number: 1001-3695(2023)12-038-3766-06

Publish History

[2023-08-08] Accepted Paper
[2023-12-05] Printed Article

Cite This Article

米德昌, 王霄, 李梦丽, 等. 灾害应急场景下基于多智能体深度强化学习的任务卸载策略 [J]. 计算机应用研究, 2023, 40 (12): 3766-3771,3777. (Mi Dechang, Wang Xiao, Li Mengli, et al. Multi-intelligence deep reinforcement learning-based task offloading strategy for disaster emergency scenarios [J]. Application Research of Computers, 2023, 40 (12): 3766-3771,3777. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

Application Research of Computers has many high-level readers and authors, and its readers are mainly senior and middle-level researchers and engineers engaged in the field of computer science, as well as teachers and students majoring in computer science and related majors in colleges and universities. Over the years, the total citation frequency and Web download rate of Application Research of Computers have been ranked among the top of similar academic journals in this discipline, and the academic papers published are highly popular among the readers for their novelty, academics, foresight, orientation and practicality.

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