AAV Swarm Cooperative Search for Moving Targets via Hybrid-Rewards Deep Reinforcement Learning

With the rapid development of low-altitude economies, unmanned aerial vehicle (UAV) swarm has attracted growing interest for cooperative target search. However, most existing studies focus on static targets and assume UAVs operate at a single horizontal altitude, limiting their practical applicability. This paper proposes a novel multi-UAV cooperative search framework for moving targets based on multi-agent deep reinforcement learning (MADRL). By coordinating UAVs across high, medium, and low-altitude layers, the system achieves improved search efficiency through altitude-adaptive operations. We further introduce a revisit-time compensation mechanism to enhance detection performance for moving targets in a multi-layer UAV swarm. To address the challenges of slow convergence and sparse feedback in MADRL, we propose hybrid-reward-based value decomposition networks (HRVDN) algorithm that integrates dense local rewards with sparse global rewards, accelerating learning while encouraging agents to collect high-value information. Simulation results demonstrate that the proposed approach outperforms existing methods in terms of target search rate and area coverage.