摘要： 面向第六代（6G）星地一体化网络的愿景，低轨（LEO）卫星边缘计算技术是实现全球无缝覆盖的关键。然而，现有研究在面对卫星网络高动态拓扑与受限星上资源时，难以有效解决计算卸载、多跳路由与资源分配之间的强耦合及高维非凸优化难题。针对此问题，本文构建了涵盖MEO、LEO卫星及地面用户的三层协作架构，并提出一种基于软演员-评论家（SAC）的分层混合优化框架（H-SAC），旨在最小化系统加权时延与能耗。为降低混合非凸问题的求解复杂度，H-SAC采用分层解耦策略，上层利用SAC智能体的最大熵机制在离散卸载空间进行充分探索，有效避免局部最优；下层则嵌入高效传统算法，求解给定卸载策略下的连续资源分配与路由规划子问题。此外，引入动态权重调整机制，使系统能根据实时服务状态自适应权衡时延与能耗目标。仿真实验表明，H-SAC在关键性能指标上显著优于H-TD3与H-DDPG，其中最终奖励分别提升约7.2%和10%。消融实验验证了ISL支持与灵活卸载机制的必要性，分别带来约18%与15%的性能增益。此外，H-SAC的推理时延较T-DRL降低约73%。总体而言，该框架能够在动态卫星边缘计算场景下实现高效且鲁棒的资源调度。

Abstract: Envisioning the sixth-generation (6G) satellite-terrestrial integrated network, Low Earth Orbit (LEO) satellite Mobile Edge Computing (MEC) is key to achieving seamless global coverage. However, existing studies struggle to effectively address the strong coupling among computation offloading, multi-hop routing, and resource allocation variables, as well as the high-dimensional non-convex optimization challenges caused by highly dynamic topologies and limited onboard resources. To address this, we establish a three-layer collaboration architecture comprising MEO, LEO, and ground users, and propose a Hierarchical Soft Actor-Critic (H-SAC) hybrid optimization framework to minimize the weighted sum of system latency and energy consumption. To reduce the complexity of solving the hybrid non-convex problem, H-SAC adopts a hierarchical decoupling strategy, the upper layer utilizes the maximum entropy mechanism of the SAC agent to fully explore the discrete offloading space, effectively avoiding local optima; the lower layer embeds efficient traditional algorithms to solve the sub-problems of continuous resource allocation and routing planning under the given offloading policy. Additionally, a dynamic weight adjustment mechanism is introduced to adaptively balance latency and energy objectives based on real-time service states. Simulation experiments demonstrate that H-SAC significantly outperforms H-TD3 and H-DDPG in key metrics, with final rewards improving by approx. 7.2% and 10%, respectively. Ablation studies verified the necessity of ISL support and flexible offloading, contributing approx. 18% and 15% performance gains. Furthermore, H-SAC reduces inference latency by approx. 73% compared to T-DRL. Overall, the framework achieves efficient and robust resource scheduling in dynamic satellite edge computing scenarios.