标题： 低空MEC的任务延迟和能耗最小化通过进化多目标深度强化学习 显示英文标题

标题： Task Delay and Energy Consumption Minimization for Low-altitude MEC via Evolutionary Multi-objective Deep Reinforcement Learning

摘要： 低空经济（LAE），由无人机（UAV）和其他飞行器驱动，已经彻底改变了交通、农业和环境监测等领域。 在即将到来的第六代（6G）时代， 无人机辅助的移动边缘计算（MEC）在山区或受灾等困难环境中尤为重要。 计算任务卸载问题是无人机辅助MEC中的关键问题，主要解决最小化任务延迟和无人机能耗之间的权衡。 在本文中，我们考虑了一个无人机辅助MEC系统，其中无人机携带边缘服务器以帮助地面设备（GDs）进行任务卸载，并制定一个计算延迟和能耗多目标优化问题（CDECMOP），以同时提高系统的性能并降低成本。 然后，通过将制定的问题建模为多目标马尔可夫决策过程（MOMDP），我们在进化框架内提出了一种多目标深度强化学习（DRL）算法，以动态调整权重并获得非支配策略。 此外，为了确保稳定的收敛并提高性能，我们结合了一种目标分布学习（TDL）算法。 仿真结果表明，所提出的算法能够更好地平衡多个优化目标，并相比其他方法获得更优的非支配解。 显示英文摘要

摘要： The low-altitude economy (LAE), driven by unmanned aerial vehicles (UAVs) and other aircraft, has revolutionized fields such as transportation, agriculture, and environmental monitoring. In the upcoming six-generation (6G) era, UAV-assisted mobile edge computing (MEC) is particularly crucial in challenging environments such as mountainous or disaster-stricken areas. The computation task offloading problem is one of the key issues in UAV-assisted MEC, primarily addressing the trade-off between minimizing the task delay and the energy consumption of the UAV. In this paper, we consider a UAV-assisted MEC system where the UAV carries the edge servers to facilitate task offloading for ground devices (GDs), and formulate a calculation delay and energy consumption multi-objective optimization problem (CDECMOP) to simultaneously improve the performance and reduce the cost of the system. Then, by modeling the formulated problem as a multi-objective Markov decision process (MOMDP), we propose a multi-objective deep reinforcement learning (DRL) algorithm within an evolutionary framework to dynamically adjust the weights and obtain non-dominated policies. Moreover, to ensure stable convergence and improve performance, we incorporate a target distribution learning (TDL) algorithm. Simulation results demonstrate that the proposed algorithm can better balance multiple optimization objectives and obtain superior non-dominated solutions compared to other methods.