Multi-UAV Redeployment Optimization Based on Multi-Agent Deep Reinforcement Learning Oriented to Swarm Performance Restoration

Distributed artificial intelligence is increasingly being applied to multiple unmanned aerial vehicles (multi-UAVs). This poses challenges to the distributed reconfiguration (DR) required for the optimal redeployment of multi-UAVs in the event of vehicle destruction. This paper presents a multi-agent deep reinforcement learning-based DR strategy (DRS) that optimizes the multi-UAV group redeployment in terms of swarm performance. To generate a two-layer DRS between multiple groups and a single group, a multi-agent deep reinforcement learning framework is developed in which a QMIX network determines the swarm redeployment, and each deep Q-network determines the single-group redeployment. The proposed method is simulated using Python and a case study demonstrates its effectiveness as a high-quality DRS for large-scale scenarios.

Automated summary

This paper presents a deep reinforcement learning-based distributed reconfiguration strategy for optimizing the redeployment of multi-UAVs, aiming to improve swarm performance. By developing a multi-agent deep reinforcement learning framework, a two-layer reconfiguration between the swarm and single groups is achieved. The effectiveness of the proposed method as a high-quality reconfiguration strategy for large-scale scenarios is demonstrated through Python simulations and a case study.