The rise of ultra-dense LEO constellations creates a complex and asynchronous network environment, driven by their massive scale, dynamic topologies, and significant delays. This unique complexity demands an adaptive packet routing algorithm that is asynchronous, risk-aware, and capable of balancing diverse and often conflicting QoS objectives in a decentralized manner. However, existing methods fail to address this need, as they typically rely on impractical synchronous decision-making and/or risk-oblivious approaches. To tackle this gap, we introduce PRIMAL, an event-driven multi-agent routing framework designed specifically to allow each satellite to act independently on its own event-driven timeline, while managing the risk of worst-case performance degradation via a principled primal-dual approach. This is achieved by enabling agents to learn the full cost distribution of the targeted QoS objectives and constrain tail-end risks. Extensive simulations on a LEO constellation with 1584 satellites validate its superiority in effectively optimizing latency and balancing load. Compared to a recent risk-oblivious baseline, it reduces queuing delay by over 70%, and achieves a nearly 12 ms end-to-end delay reduction in loaded scenarios. This is accomplished by resolving the core conflict between naive shortest-path finding and congestion avoidance, highlighting such autonomous risk-awareness as a key to robust routing.

翻译：超密集低轨星座的兴起，因其大规模、动态拓扑和显著延迟，催生了一个复杂且异步的网络环境。这种独特的复杂性要求一种自适应的数据包路由算法，该算法需具备异步性、风险感知能力，并能够以去中心化的方式平衡多样且常相互冲突的QoS目标。然而，现有方法未能满足这一需求，因为它们通常依赖于不切实际的同步决策和/或忽视风险的方法。为弥补这一空白，我们提出了PRIMAL，一个事件驱动的多智能体路由框架，专门设计用于允许每颗卫星在其自身的事件驱动时间线上独立行动，同时通过一种原理性的原始-对偶方法来管理最坏情况性能下降的风险。这是通过使智能体能够学习目标QoS目标的完整成本分布并约束尾部风险来实现的。在一个包含1584颗卫星的低轨星座上进行的大量仿真验证了其在有效优化延迟和平衡负载方面的优越性。与近期一个忽视风险的基线相比，它将排队延迟降低了超过70%，并在负载场景下实现了近12毫秒的端到端延迟减少。这是通过解决朴素最短路径查找与拥塞避免之间的核心冲突而实现的，突显了这种自主风险感知作为实现稳健路由的关键。