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Algorithm Research & Explore
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App. Res. of Comp. Printed Article
Vol. 39, 2022 No. 7
2087-2090,2103

Dynamic mobile robot collaborative computing offloading based on improved deep reinforcement learning

Li Shaobo1,2,3,4
Liu Yiyang1,2,3
1. Key Laboratory of Networked Control Systems, Chinese Academy of Sciences, Shenyang 110016, China
2. Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China
3. Institutes for Robotics & Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110169, China
4. University of Chinese Academy of Sciences, Beijing 100049, China
DOI: 10.19734/j.issn.1001-3695.2021.11.0654

Abstract

Mobile edge computing is a method to solve the computing-intensive task requirements of robots. Traditional algorithms are based on intelligent algorithms or convex optimization methods, and the iteration time is long. Deep reinforcement learning can be solved in a single forward pass, but only for a fixed number of robots. Through the analysis and research of deep reinforcement learning, this paper performed input regularization before the input layer in the deep reinforcement learning neural network, and added a convolution layer after the output layer so that the network could adaptively meet the unloading requirements of the number of dynamic mobile robots. Finally, it carried out the simulation experiments to verify the effectiveness and feasibility of proposed algorithm, compared with the adaptive genetic algorithm and reinforcement learning.

Foundation Support

国家自然科学基金资助项目(U1908212,6101020101)
辽宁省兴辽英才项目(XLYC1907057)
国家重点研发项目(2018YFB1700200)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2021.11.0654
Publish at: Application Research of Computers Printed Article, Vol. 39, 2022 No. 7
Section: Algorithm Research & Explore
Pages: 2087-2090,2103
Serial Number: 1001-3695(2022)07-026-2087-04

Publish History

[2022-02-16] Accepted Paper
[2022-07-05] Printed Article

Cite This Article

李少波, 刘意杨. 基于改进深度强化学习的动态移动机器人协同计算卸载 [J]. 计算机应用研究, 2022, 39 (7): 2087-2090,2103. (Li Shaobo, Liu Yiyang. Dynamic mobile robot collaborative computing offloading based on improved deep reinforcement learning [J]. Application Research of Computers, 2022, 39 (7): 2087-2090,2103. )

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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