Unified multi-agent recovery framework via multi-scale diffusion and dependency-aware hierarchical PPO for resilience enhancement

Abstract Enhancing resilience and robustness in multi-agent systems (MAS) under complex attack patterns and dynamic environments remains a formidable challenge. This paper proposes the Graph Diffusion Reinforcement Recovery (GDRR) framework, which integrates multi-scale diffusion (MSD) embedding with dependency-aware hierarchical proximal policy optimization (DAPPO) to optimize resilience under multi-dimensional perturbations. MSD captures both local and global structural dependencies by performing multi-hop information diffusion over heterogeneous graph topologies, thereby improving feature robustness and fault tolerance. DAPPO dynamically prunes the action space by evaluating the importance of collaboration chains, enabling the agent to prioritize critical recovery operations. Experimental evaluations on cooperative swarm systems, such as simulated UAV swarm networks, demonstrate that GDRR outperforms existing methods by 1.54% to 2.91% in resilience under 50% attack intensity across four distinct adversarial scenarios. These results highlight that the collaborative integration of MSD and DAPPO enables GDRR to achieve enhanced resilience and rapid recovery in real-world MAS deployments.