The swarm system is a kind of multi-agent system where amazing “emergence” phenomenon can be observed. In recent years, biologists have made great effort to model the self-organizing rules of collective behaviors. By analyzing the data collected from biological swarm systems, a plenty of models have been built to reproduce and explain the collective behaviors. Data-driven methods are also promising ways to model collective behaviors. Imitation learning (IL), as a data-driven method, can learn the potential model from the expert data. Further, the Generative Adversarial Imitation Learning (GAIL) is a popular imitation learning method and has been used in many fields as auto-driving, machine translation and image generation. In this paper, we introduce a Population-based Training (PBT) method to GAIL to enhance the performance of GAIL. Moreover, we use this PBT-GAIL framework to model the self-organizing rules according to the simulation data of the icsek model. Simulation results show that the proposed PBT-GAL framework is effective when imitating the icsek model. Besides, the PBT-GAIL framework has better convergence tendency and alignment than that of GAIL.


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    Titel :

    PBT-GAIL: An Imitation Learning Framework in Swarm Systems


    Weitere Titelangaben:

    Lect. Notes Electrical Eng.


    Beteiligte:
    Wu, Meiping (Herausgeber:in) / Niu, Yifeng (Herausgeber:in) / Gu, Mancang (Herausgeber:in) / Cheng, Jin (Herausgeber:in) / Liu, Shuo (Autor:in) / Peng, Xingguang (Autor:in) / Wang, Tonghao (Autor:in)

    Kongress:

    International Conference on Autonomous Unmanned Systems ; 2021 ; Changsha, China September 24, 2021 - September 26, 2021



    Erscheinungsdatum :

    2022-03-18


    Format / Umfang :

    11 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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