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

TL;DR

This paper introduces a new multi-agent pursuit method combining self-organizing feature maps and reinforcement learning to improve group coordination and efficiency in capturing evaders.

Contribution

It proposes a novel integration of SOFM and AGRMF-based reinforcement learning for dynamic group organization in pursuit scenarios.

Findings

More effective evader capture by mobile agents

Faster convergence of neural network through AGR membership updates

Improved group reorganization during pursuit

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.