关键词:
edge information system
internet of vehicles
distributed machine learning
deep reinforcement learning
worker selection
摘要:
Nowadays,Edge Information System(EIS)has received a lot of *** EIS,Distributed Machine Learning(DML),which requires fewer computing resources,can implement many artificial intelligent applications ***,due to the dynamical network topology and the fluctuating transmission quality at the edge,work node selection affects the performance of DML a *** this paper,we focus on the Internet of Vehicles(IoV),one of the typical scenarios of EIS,and consider the DML-based High Definition(HD)mapping and intelligent driving decision model as the *** worker selection problem is modeled as a Markov Decision Process(MDP),maximizing the DML model aggregate performance related to the timeliness of the local model,the transmission quality of model parameters uploading,and the effective sensing area of the worker.A Deep Reinforcement Learning(DRL)based solution is proposed,called the Worker Selection based on Policy Gradient(PG-WS)*** policy mapping from the system state to the worker selection action is represented by a deep neural *** episodic simulations are built and the REINFORCE algorithm with baseline is used to train the policy *** show that the proposed PG-WS algorithm outperforms other comparation methods.