Low Earth Orbit (LEO) satellite networks provide global coverage and low latency, yet high node mobility, uneven traffic distribution, and stochastic link failures pose severe challenges for inter-domain routing. Existing approaches either neglect graph-structured topology or lack dynamic awareness of real-time link states, struggling to balance load distribution and routing reliability. This paper proposes DTAR, a traffic-aware deep reinforcement learning approach for inter-domain routing in LEO satellite networks. A multi-objective NSGA-II algorithm first generates an offline domain partition maximizing intra-domain traffic ratio and minimizing load imbalance. A Graph Attention Network dynamically encodes inter-domain link traffic intensity, load distribution, and fault status, upon which an action-masked PPO agent learns routing decisions online. Simulations on a 288-satellite Walker constellation against multiple baselines demonstrate that DTAR significantly reduces link load imbalance and end-to-end delay, while improving routing success rate and reducing packet loss rate across normal, traffic surge, and fault scenarios.

翻译：低地球轨道（LEO）卫星网络提供全球覆盖和低延迟，但高节点移动性、不均匀的流量分布和随机链路失效对域间路由提出了严峻挑战。现有方法要么忽略图结构拓扑，要么缺乏对实时链路状态的动态感知，难以平衡负载分布和路由可靠性。本文提出DTAR，一种面向LEO卫星网络域间路由的流量感知深度强化学习方法。多目标NSGA-II算法首先生成离线域划分，最大化域内流量比例并最小化负载不均衡。图注意力网络动态编码域间链路流量强度、负载分布和故障状态，在此基础上，带动作掩蔽的PPO智能体在线学习路由决策。在包含288颗卫星的Walker星座上的仿真实验表明，与多个基线方法相比，DTAR在正常、流量激增和故障场景下显著降低了链路负载不均衡和端到端延迟，同时提高了路由成功率并降低了丢包率。