Ensuring continuous service coverage under unexpected hardware failures is a fundamental challenge for 3D Aerial-Ground Integrated Networks. Although Multi-Agent Reinforcement Learning facilitates autonomous coordination, traditional architectures often lack resilience to sudden topology deformations. This paper proposes the Topology-Aware Graph MAPPO (TAG-MAPPO) framework to enhance system survivability through autonomous 3D spatial reconfiguration. Our framework integrates graph-based feature aggregation with a residual ego-state fusion mechanism to capture intricate inter-agent dependencies. To achieve structural robustness, we introduce a Random Observation Shuffling mechanism that fosters strong generalization to agent population fluctuations by breaking coordinate-index dependencies. Extensive simulations across heterogeneous environments, including high-speed mobility at 15 meters per second, demonstrate that TAG-MAPPO significantly outperforms Multi-Layer Perceptron baselines. Specifically, the framework reduces redundant handoffs by up to 50 percent while maintaining superior energy efficiency. Most notably, TAG-MAPPO exhibits exceptional self-healing capabilities, restoring over 90 percent of pre-failure coverage within 15 time steps. In dense urban scenarios, the framework achieves a post-failure fairness index surpassing its original four-UAV configuration by autonomously resolving service overlaps and interference. These findings confirm that topology-aware coordination is essential for resilient 6G aerial networks.

翻译：在三维空天地一体化网络中，确保意外硬件故障下的持续服务覆盖是一项基础性挑战。尽管多智能体强化学习促进了自主协同，但传统架构通常缺乏应对突发拓扑形变的弹性。本文提出拓扑感知图MAPPO框架，通过自主三维空间重构来增强系统生存能力。该框架将基于图的特征聚合与残差自状态融合机制相结合，以捕捉复杂的智能体间依赖关系。为实现结构鲁棒性，我们引入随机观测混洗机制，通过打破坐标索引依赖性，促进对智能体数量波动的强泛化能力。在包括每秒15米高速移动在内的异构环境中进行的广泛仿真表明，TAG-MAPPO显著优于多层感知机基线。具体而言，该框架在保持卓越能效的同时，将冗余切换减少高达50%。最值得注意的是，TAG-MAPPO展现出卓越的自愈能力，能在15个时间步内恢复超过90%的故障前覆盖范围。在密集城市场景中，该框架通过自主解决服务重叠与干扰，使故障后公平性指数超越其原始四无人机配置。这些发现证实，拓扑感知协同对于弹性6G空中网络至关重要。