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A novel approach for multi-agent cooperative pursuit to capture grouped evaders

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Abstract

An approach of mobile multi-agent pursuit based on application of self-organizing feature map (SOFM) and along with that reinforcement learning based on agent group role membership function (AGRMF) model is proposed. This method promotes dynamic organization of the pursuers’ groups and also makes pursuers’ group evader according to their desire based on SOFM and AGRMF techniques. This helps to overcome the shortcomings of the pursuers that they cannot fully reorganize when the goal is too independent in process of AGRMF models operation. Besides, we also discuss a new reward function. After the formation of the group, reinforcement learning is applied to get the optimal solution for each agent. The results of each step in capturing process will finally affect the AGR membership function to speed up the convergence of the competitive neural network. The experiments result shows that this approach is more effective for the mobile agents to capture evaders.

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Authors and Affiliations

  1. Department of Computer Science and Technology, Harbin Institute of Technology, Harbin, China

    Muhammad Zuhair Qadir & Songhao Piao

  2. Department of Mathematics, Harbin Institute of Technology, Harbin, China

    Haiyang Jiang

  3. Department of Computer Science, University of Khenchela, Khenchela, Algeria

    Mohammed El Habib Souidi

Authors
  1. Muhammad Zuhair Qadir
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  2. Songhao Piao
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  3. Haiyang Jiang
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  4. Mohammed El Habib Souidi
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Corresponding author

Correspondence to Songhao Piao.

Additional information

This study was supported by National Natural Science Foundation of China [Grant Number 61375081] and a special fund project of Harbin science and technology innovation talents research [Grant Number RC2013XK010002].

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Qadir, M.Z., Piao, S., Jiang, H. et al. A novel approach for multi-agent cooperative pursuit to capture grouped evaders. J Supercomput 76, 3416–3426 (2020). https://doi.org/10.1007/s11227-018-2591-3

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  • DOI: https://doi.org/10.1007/s11227-018-2591-3

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