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MACA: Multi-Agent with Credit Assignment for Computation Offloading in Smart Parks Monitoring

Author

Listed:
  • Liang She

    (School of Computer Science and Engineering, Central South University, Changsha 410083, China
    School of Computer Science, Hunan University of Technology and Business, Changsha 410205, China)

  • Jianyuan Wang

    (School of Automation Science and Electrical Engineering, Beihang University, Beijing 100191, China)

  • Yifan Bo

    (School of Computer Science and Engineering, Beihang University, Beijing 100191, China)

  • Yangyan Zeng

    (School of Frontier Crossover Studies, Hunan University of Technology and Business, Changsha 410205, China)

Abstract
Video monitoring has a wide range of applications in a variety of scenarios, especially in smart parks. How to improve the efficiency of video data processing and reduce resource consumption have become of increasing concern. The high complexity of traditional computation offloading algorithms makes it difficult to apply them to real-time decision-making scenarios. Thus, we propose a multi-agent deep reinforcement learning algorithm with credit assignment (MACA) for computation offloading in smart park monitoring. By making online decisions after offline training, the agent can give consideration to both decision time and accuracy in effectively solving the problem of the curse of dimensionality. Via simulation, we compare the performance of MACA with traditional deep Q-network reinforcement learning algorithm and other methods. Our results show that MACA performs better in scenarios where there are a higher number of agents and can minimize request delay and reduce task energy consumption. In addition, we also provide results from a generalization capability verified experiment and ablation study, which demonstrate the contribution of MACA algorithm to each component.

Suggested Citation

  • Liang She & Jianyuan Wang & Yifan Bo & Yangyan Zeng, 2022. "MACA: Multi-Agent with Credit Assignment for Computation Offloading in Smart Parks Monitoring," Mathematics, MDPI, vol. 10(23), pages 1-18, December.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:23:p:4616-:d:994697
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    References listed on IDEAS

    as
    1. Saeed Nosratabadi & Amir Mosavi & Puhong Duan & Pedram Ghamisi, 2020. "Data Science in Economics," Papers 2003.13422, arXiv.org.
    2. Nosratabadi, Saeed & Mosavi, Amir & Duan, Puhong & Ghamisi, Pedram & Filip, Ferdinand & Band, Shahab S. & Reuter, Uwe & Gama, Joao & Gandomi, Amir H., 2020. "Data science in economics: comprehensive review of advanced machine learning and deep learning methods," MetaArXiv haf2v, Center for Open Science.
    3. Feng Zeng & Jiangjunzhe Tang & Chengsheng Liu & Xiaoheng Deng & Wenjia Li, 2022. "Task-Offloading Strategy Based on Performance Prediction in Vehicular Edge Computing," Mathematics, MDPI, vol. 10(7), pages 1-19, March.
    4. Nosratabadi, Saeed & Mosavi, Amir & Duan, Puhong & Ghamisi, Pedram & Filip, Ferdinand & Band, Shahab S. & Reuter, Uwe & Gama, Joao & Gandomi, Amir H., 2020. "Data science in economics: comprehensive review of advanced machine learning and deep learning methods," SocArXiv 9vdwf, Center for Open Science.
    5. Nosratabadi, Saeed & Mosavi, Amir & Duan, Puhong & Ghamisi, Pedram & Filip, Ferdinand & Band, Shahab S. & Reuter, Uwe & Gama, Joao & Gandomi, Amir H., 2020. "Data science in economics: comprehensive review of advanced machine learning and deep learning methods," Thesis Commons auyvc, Center for Open Science.
    Full references (including those not matched with items on IDEAS)

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