In 3D Aerial-Ground Integrated Networks (AGINs), ensuring continuous service coverage under unexpected hardware failures is critical for mission-critical applications. While Multi-Agent Reinforcement Learning (MARL) has shown promise in autonomous coordination, its resilience under sudden node failures remains a challenge due to dynamic topology deformation. This paper proposes a Topology-Aware Graph MAPPO (TAG-MAPPO) framework designed to enhance system survivability through autonomous 3D spatial reconfiguration. Our framework incorporates graph-based feature aggregation with a residual ego-state fusion mechanism to capture intricate inter-agent dependencies. This architecture enables the surviving swarm to rapidly adapt its topology compared to conventional Multi-Layer Perceptron (MLP) based approaches. Extensive simulations across heterogeneous environments, ranging from interference-limited Crowded Urban to sparse Rural areas, validate the proposed approach. The results demonstrate that TAG-MAPPO consistently outperforms baselines in both stability and efficiency; specifically, it reduces redundant handoffs by up to 50 percent while maintaining a lead in energy efficiency. Most notably, the framework exhibits exceptional self-healing capabilities following a catastrophic node failure. TAG-MAPPO restores over 90 percent of the pre-failure service coverage within 15 time steps, exhibiting a significantly faster V-shaped recovery trajectory than MLP baselines. Furthermore, in dense urban scenarios, the framework achieves a post-failure Jain's Fairness Index that even surpasses its original four-UAV configuration by effectively resolving service overlaps. These findings suggest that topology-aware coordination is essential for the realization of resilient 6G aerial networks and provides a robust foundation for adaptive deployments in volatile environments.

翻译：在三维空天地一体化网络（AGINs）中，为关键任务应用确保硬件突发故障下的连续服务覆盖至关重要。尽管多智能体强化学习（MARL）在自主协同方面展现出潜力，但在突发节点故障下，由于动态拓扑形变，其弹性仍面临挑战。本文提出了一种拓扑感知图MAPPO（TAG-MAPPO）框架，旨在通过自主三维空间重构来增强系统生存能力。我们的框架结合了基于图的特征聚合与残差自我状态融合机制，以捕捉智能体间复杂的相互依赖关系。与传统的基于多层感知机（MLP）的方法相比，该架构使幸存集群能够快速适应其拓扑结构。在从干扰受限的拥挤城市到稀疏农村的多种异构环境中的广泛仿真验证了所提方法。结果表明，TAG-MAPPO在稳定性和效率方面均持续优于基线方法；具体而言，它在保持能效领先的同时，将冗余切换减少了高达50%。最值得注意的是，该框架在灾难性节点故障后展现出卓越的自愈能力。TAG-MAPPO在15个时间步内恢复了超过90%的故障前服务覆盖，其V形恢复轨迹显著快于MLP基线。此外，在密集城市场景中，该框架通过有效解决服务重叠，实现的故障后Jain公平指数甚至超过了其原始的四无人机配置。这些发现表明，拓扑感知协同对于实现弹性的6G空中网络至关重要，并为在多变环境中的自适应部署提供了坚实基础。