Multi-UAV Roundup Inspired by Hierarchical Cognition Consistency Learning Based on an Interaction Mechanism

Abstract

This paper is concerned with the problem of multi-UAV roundup inspired by hierarchical cognition consistency learning based on an interaction mechanism. First, a dynamic communication model is constructed to address the interactions among multiple agents. This model includes a simplification of the communication graph relationships and a quantification of information efficiency. Then, a hierarchical cognition consistency learning method is proposed to improve the efficiency and success rate of roundup. At the same time, an opponent graph reasoning network is proposed to address the prediction of targets. Compared with existing multi-agent reinforcement learning (MARL) methods, the method developed in this paper possesses the distinctive feature that target assignment and target prediction are carried out simultaneously. Finally, to verify the effectiveness of the proposed method, we present extensive experiments conducted in the scenario of multi-target roundup. The experimental results show that the proposed architecture outperforms the conventional approach with respect to the roundup success rate and verify the validity of the proposed model.

Keywords

• multi-target roundup
• neighborhood cognitive consistency
• opponent graph reasoning network
• hierarchical cognitive consistency learning