In vehicle edge computing systems,individual vehicles frequently encounter difficulties in executing computation-intensive tasks due to their constrained processing capacities.Furthermore,the highly dynamic nature of the Internet of Vehicles(IoV)environment exacerbates the challenge,as vehicles struggle to gather comprehensive global information about their surroundings and the task offloading behaviors of neighboring vehicles.This complexity hampers effective decision-making in task offloading.To mitigate these challenges—limited computational resources,fluctuating environmental conditions,and restricted observational capabilities—this study introduces an online algorithm based on the Multi-Agent Deep Deterministic Policy Gradient(MADDPG)framework.The proposed approach synergistically integrates both Vehicle-to-Infrastructure(V2I)and Vehicle-to-Vehicle(V2V)offloading mechanisms while also incorporating task division to optimize overall system performance.First,by considering vehicle location,connection duration,and available computational resources,the vehicle with the highest service performance value is selected as the candidate service vehicle.Second,an optimization problem is formulated to minimize the system's average task offloading delay,and this problem is modeled as a Markov decision process.Through centralized training,vehicles are able to obtain information from other vehicles,enabling them to adjust their own policies accordingly.In the online execution phase,vehicles can make rapid task offloading decisions based on local observations.Finally,the proposed algorithm is compared against benchmark algorithms.The experimental results demonstrate that,compared to the deep deterministic policy gradient method and the equal task division method,the proposed task offloading algorithm reduces the average task offloading delay by 75％and 66％,respectively,and exhibits a faster convergence rate,validating the algorithm's effectiveness.
关键词
车辆边缘计算/任务卸载/任务划分/多智能体深度强化学习/V2I与V2V联合卸载
Key words
vehicle edge computing/task offloading/task division/multi-agent deep reinforcement learning/V2I and V2V joint offloading
维普
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