信息与控制2025，Vol.54Issue(2)：288-298,11.DOI:10.13976/j.cnki.xk.2024.1882
基于不确定性感知探索的近端策略优化算法的无人机辅助移动边缘计算与缓存优化
Drone-assisted Mobile Edge Computing and Caching Optimization Based on Uncertainty-aware Exploration in Proximal Policy Optimization Algorithm
摘要
Abstract
To address the limitations of traditional edge computing and caching technologies in handling computationally intensive and latency-sensitive tasks,we propose an active edge computing and caching optimization scheme centered on unmanned aerial vehicles(UAVs).The scheme leverages UAVs to actively sense vehicle demands,enhancing the accuracy of road vehicle demand prediction by integrating binary classification mathematical models with Hawkes processes.The problem is formulated as a Markov decision process,and to optimize edge caching and task offloading,an uncertainty-aware exploration proximal policy optimization(UAE-PPO)algorithm is introduced,building upon improvements to the proximal policy optimization(PPO)algorithm.The UAE-PPO algorithm enhances model stability and generalization by incorporating uncertainty-aware exploration and dynamically adjusting exploration strategies within the actor network.Additionally,it integrates adaptive attenuation of the clip parameter and L2 regularization techniques.Simulation results demonstrate that,compared to the traditional PPO algorithm,the proposed UAE-PPO algorithm improves reward convergence speed by 28.6%and increases the re-ward value by 6.3%.关键词
移动边缘计算/任务卸载/近端策略优化算法/缓存优化Key words
mobile edge computing/task offloading/proximal policy optimization algorithm/cache optimization分类
计算机与自动化引用本文复制引用
谢键,于思源,张旭秀..基于不确定性感知探索的近端策略优化算法的无人机辅助移动边缘计算与缓存优化[J].信息与控制,2025,54(2):288-298,11.基金项目
辽宁省重点研发计划(2022020594-JH1/108) （2022020594-JH1/108）
