Abstract: In order to deal with the core challenges faced by the task offloading decision in the UAV assisted mobile edge computing system, such as multi-dimensional timing coupling, dynamic environment adaptation and insufficient strategy robustness, this paper innovatively proposes a dual delay depth deterministic strategy gradient algorithm (HTAN-TD3) that integrates hierarchical timing attention mechanism. The breakthrough contributions of this study are reflected in three aspects: firstly, a composite optimization objective that integrates total system latency, worst user experience, and multi-user fairness is constructed, which breaks through the limitations of traditional single objective modeling; Secondly, a hierarchical attention network (HTAN) with macro micro dual stream temporal analysis capability was designed. Through the heterogeneous collaboration and attention weighted fusion of LSTM and GRU, accurate perception and deep mining of dynamic features at multiple time scales in the system state were achieved; Furthermore, the Ornstein Uhlenbeck process with temporal correlation is introduced to explore the noise and dynamic adaptive Huber loss function, and the algorithm is systematically enhanced from two dimensions: policy exploration smoothness and training process robustness. In a complex edge scene simulating high load, strong occlusion and multi-user competition, HTAN-TD3 is significantly superior to mainstream baseline algorithms such as DDPG and TD3 and MATOPO in key indicators such as total system delay and user fairness, demonstrating excellent environmental adaptability and decision-making intelligence. This study provides a useful reference and reference for improving the autonomous decision-making ability of intelligent edge computing systems in dynamic and complex environments.

摘要： 为应对无人机辅助移动边缘计算系统中任务卸载决策面临的多维时序耦合、动态环境适配与策略稳健性不足等核心挑战，本文创新性地提出一种融合分层时序注意力机制的双延迟深度确定性策略梯度算法（HTAN-TD3）。本研究的突破性贡献体现在三个方面：首先，构建了一种融合系统总时延、最差用户体验与多用户公平性的复合优化目标，突破了传统单目标建模的局限性；其次，设计了具备宏观-微观双流时序解析能力的分层注意力网络（HTAN），通过LSTM与GRU的异构协同与注意力加权融合，实现了对系统状态中多时间尺度动态特征的精准感知与深度挖掘；进一步，引入具有时序相关性的Ornstein-Uhlenbeck过程探索噪声与动态自适应Huber损失函数，分别从策略探索平滑性与训练过程鲁棒性两个维度对算法进行系统性增强。在模拟高负载、强遮挡与多用户竞争的复杂边缘场景中，HTAN-TD3在系统总时延与用户公平性等关键指标上显著优于DDPG、TD3、MATOPO等主流基线算法，展现出卓越的环境适应性与决策智能性，本研究为提升智能边缘计算系统在动态复杂环境下的自主决策能力提供了有益的参考与借鉴。