计算机应用与软件2024，Vol.41Issue(7)：239-245,341,8.DOI:10.3969/j.issn.1000-386x.2024.07.035
基于强化学习的并行任务实时调度方法
REAL-TIME SCHEDULING OF PARALLEL TASKS BASED ON REINFORCEMENT LEARNING
摘要
Abstract
Existing parallel tasks scheduling algorithms do not consider the environmental instability,adaptability and real-time performance simultaneously.In view of this,we propose a parallel tasks scheduling algorithm based on reinforcement learning.It took the scheduling process as a Markov decision process.Through the interactions between agents and the environment,policies were optimized by the proximal policy optimization method.A simulation method was used to construct the reward function.By adding empirical terms to the advantage estimators by denoising autoencoders,the agents could learn efficient and reliable arrangement policies.The results of simulation experiments in two scenarios show that the proposed method can schedule in milliseconds,and improve the time utilization by more than 17％and the output by more than 16％compared with existing algorithms.关键词
强化学习/并行任务/实时调度/仿真/降噪自编码器Key words
Reinforcement learning/Parallel task/Real-time schedule/Simulation/Denoising autoencoder分类
信息技术与安全科学引用本文复制引用
王泽远..基于强化学习的并行任务实时调度方法[J].计算机应用与软件,2024,41(7):239-245,341,8.基金项目
国家电网公司科技项目(SGBJDKOODWJS2000117). （SGBJDKOODWJS2000117）
