Multi-Agent Deep Reinforcement Learning (MADRL)-Based End-to-End Formation Control for UAV Swarm with Dynamic Topology

While MADRL has demonstrated significant potential in Unmanned Aerial Vehicle (UAV) swarm control, traditional architectures often rely on fixed-dimensional observation spaces. This rigid structural constraint severely limits the swarm’s adaptability in dynamic environments, particularly when facing sudden topological changes such as node failures or dynamic reinforcements. To overcome these limitations, this paper proposes an end-to-end UAV swarm motion control framework incorporating a state-modulated Graph Attention Network (GAT). By modeling the swarm as a dynamic interaction graph, the proposed method dynamically aggregates neighbor features using attention weights modulated by the agents’ real-time kinematic states. Furthermore, a virtual structure combined with an auction mechanism is introduced to achieve precise formation planning and target allocation. Evaluated in the Genesis 3D physics engine, the proposed Prioritized Experience Replay (PER)-MADDPG-Graph Attention Network (GAT) algorithm exhibits superior robustness and spatial adaptability. Extensive experiments, including dynamic node reduction and addition scenarios, confirm that the proposed framework seamlessly maintains swarm configurations without catastrophic policy degradation, outperforming baseline MADRL methods in both convergence speed and control precision.