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Vehicle Edge Computing Network Service Migration Strategy Based on Multi-agent Reinforcement Learning

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Computer Supported Cooperative Work and Social Computing (ChineseCSCW 2023)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 2013))

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Abstract

In order to address the decision-making problem in vehicular edge computing networks, this paper presented a vehicle edge computing service migration strategy based on multi-agent deep learning. This strategy employs the multi-agent deep deterministic policy gradient algorithm, enabling vehicles to learn from incomplete system information and make distributed online migration decisions with only partial observations. Users collaborate and compete to achieve common goals, making the system more flexible and enhancing the overall benefits and stability. Simulation results using real datasets demonstrate that the proposed strategy converges rapidly and exhibits superior performance, robustness, and stability in scenarios with varying numbers of users and task arrival rates compared to other benchmark strategies.

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Acknowledgment

These works were supported by the National Natural Science Foundation of China (No. 62062008,61762010).

Author information

Authors and Affiliations

  1. School of Information Engineering, Nanning University, 8 Longting Road, Nanning, China

    Zhongli Chen, Jichang Chen & Taoshen Li

  2. School of Computer, Electronics and Information, Guangxi University, 100 Daxue Road, Nanning, China

    Zhongli Chen & Taoshen Li

Authors
  1. Zhongli Chen
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  2. Jichang Chen
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  3. Taoshen Li
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Corresponding author

Correspondence to Taoshen Li .

Editor information

Editors and Affiliations

  1. Shandong University, Jinan, China

    Yuqing Sun

  2. Fudan University, Shanghai, China

    Tun Lu

  3. Harbin Engineering University, Harbin, China

    Tong Wang

  4. Tongji University, Shanghai, China

    Hongfei Fan

  5. Guangdong University of Technology, Guangzhou, China

    Dongning Liu

  6. Tongji University, Shanghai, China

    Bowen Du

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© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Chen, Z., Chen, J., Li, T. (2024). Vehicle Edge Computing Network Service Migration Strategy Based on Multi-agent Reinforcement Learning. In: Sun, Y., Lu, T., Wang, T., Fan, H., Liu, D., Du, B. (eds) Computer Supported Cooperative Work and Social Computing. ChineseCSCW 2023. Communications in Computer and Information Science, vol 2013. Springer, Singapore. https://doi.org/10.1007/978-981-99-9640-7_35

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  • DOI: https://doi.org/10.1007/978-981-99-9640-7_35

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-99-9639-1

  • Online ISBN: 978-981-99-9640-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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