Abstract: In remote and disaster-stricken areas, ground Internet of Things (IoT) devices are constrained by limited computing capabilities and insufficient communication infrastructure, making it difficult to support a large number of emergency tasks with stringent latency requirements within a short time. Existing studies mainly adopt single unmanned aerial vehicle (UAV) or low Earth orbit (LEO) satellite architectures, or treat UAVs merely as communication relay nodes, and their optimization objectives primarily focus on minimizing system latency or a weighted sum of latency and energy consumption, failing to fully exploit the cooperative computing potential of multiple UAVs and multiple LEO satellites as well as to satisfy the heterogeneous quality-of-service (QoS) requirements arising from different task priorities and latency constraints. Therefore, this paper proposes a multi-agent deep reinforcement learning–based task offloading and adaptive resource allocation strategy, termed TOARA. First, a space–air–ground integrated network (SAGIN) architecture with cooperative multiple UAVs and multiple LEO satellites is constructed and integrated with edge computing technologies to effectively alleviate ground resource limitations. In this architecture, UAVs collect ground tasks and make intelligent offloading decisions, dynamically assigning tasks to local edge nodes or LEO satellite nodes for execution. Then, the joint task offloading and resource allocation problem is formulated as a decentralized partially observable Markov decision process and solved using a multi-agent deep deterministic policy gradient (MADDPG) algorithm under a centralized training and decentralized execution framework, enabling agents to autonomously learn efficient offloading decisions and adaptive resource allocation strategies to jointly optimize task processing latency, system energy consumption, and the completion rates of tasks with different priority levels. Finally, simulation results demonstrate that, compared with several baseline strategies, the proposed algorithm reduces the average task processing latency and system energy consumption by at least 26.09% and 27.53%, respectively, while improving the completion rate of high-priority tasks by at least 22.24%, validating its effectiveness in learning efficient task offloading and resource allocation decisions in dynamic and complex environments.

摘要： 针对偏远及灾害地区地面物联网（IoT）设备计算能力受限、通信基础设施不足，难以在短时间内支撑大量具有严格时延约束的紧急任务处理问题，现有研究多采用单独无人机（UAV）或低轨（LEO）卫星架构，或仅将UAV作为通信中继节点，且优化目标主要侧重系统时延或时延与能耗的加权和，未能充分考虑多UAV与多LEO卫星协同计算潜力以及不同任务优先级和时延约束的差异化服务质量需求。因此，本文提出了一种基于多智能体深度强化学习的任务卸载和自适应资源分配策略（TOARA）。首先，构建了多UAV和多LEO卫星协同的空天地一体化网络（SAGIN）架构，并将该架构与边缘技术相结合，有效缓解了地面资源受限问题。其中，UAV负责收集地面任务并进行智能卸载决策，将任务动态分配到本地边缘节点或LEO卫星节点进行处理。其次将上述问题建模为分布式部分可观察马尔可夫决策过程并采用基于多智能体深度确定性策略梯度（MADDPG）方法求解，该策略采用基于集中式训练-分布式执行的训练框架，使各智能体能够自主学习高效的卸载决策和动态资源分配，优化任务处理时延、系统能耗及不同优先级任务完成率等多个目标。最后，仿真结果表明，相较于多种基线策略，该算法的任务处理平均时延和系统能耗分别至少降低26.09%和27.53%，高优先级任务完成率至少提升22.24%，验证了该算法在动态复杂的环境下高效学习任务卸载和资源分配决策的有效性。