The utilization of heterogeneous end devices such as the low earth orbit (LEO) satellite, unmanned aerial vehicles (UAVs) and ground users (GUs) deployed at different altitudes, known as the space-air-ground integrated network (SAGI-Net), can be quite promising towards a bunch of advanced applications. Whereas, the energy efficiency of the SAGI-Net communication system is a key criterion needed to be improved urgently in consideration that the inappropriate communication routing will undoubtedly cause a huge communication energy cost of the system especially with a large number of communication devices inside. In this paper, we proposed a novel communication routing selection model for the SAGI-Net system and established a heterogeneous multi-agent reinforcement learning (HMF-MARL) framework to optimize the communication energy efficiency of this system, where the mean-field theory was introduced to enhance the ability of classic MARL method while still maintaining a relatively low computational complexity. The experiment results show that the capacity of the heterogeneous multi-agent system has been improved by nearly 80% using the proposed HMF-MARL method compared with the classic MARL one, which hopefully shows the potential value on the implementation of the SAGI-Net system in the future.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Heterogeneous Mean-Field Multi-Agent Reinforcement Learning for Communication Routing Selection in SAGI-Net


    Contributors:
    Zhang, Hengxi (author) / Tang, Huaze (author) / Hu, Yuanquan (author) / Wei, Xiaoli (author) / Wu, Chenye (author) / Ding, Wenbo (author) / Zhang, Xiao-Ping (author)


    Publication date :

    2022-09-01


    Size :

    1872948 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Sertifikacija: pervye sagi perspektivy

    Gluchov, I. A. / Matjusin, V. A. / Solovév, I. P. | IuD Bahn | 2000


    3M-RL: Multi-Resolution, Multi-Agent, Mean-Field Reinforcement Learning for Autonomous UAV Routing

    Wang, Weichang / Liu, Yongming / Srikant, Rayadurgam et al. | IEEE | 2022


    Fair collaborative vehicle routing: A deep multi-agent reinforcement learning approach

    Mak, Stephen / Xu, Liming / Pearce, Tim et al. | Elsevier | 2023


    Communication-efficient and federated multi-agent reinforcement learning

    Krouka, M. (Mounssif) / Elgabli, A. (Anis) / Issaid, C. B. (Chaouki Ben) et al. | BASE | 2022

    Free access