电讯技术2024，Vol.64Issue(1)：58-66,9.DOI:10.20079/j.issn.1001-893x.230103001
基于DDPG的综合化航电系统多分区任务分配优化方法
A DDPG-based Optimization Method for Multi-partition Task Assignment of IMA
摘要
Abstract
The integrated modular avionics(IMA)system implements the integration of multiple avionics functions under a shared resource platform through a spatio-temporal partitioning mechanism.The merit of the task distribution method between partitions determines the overall effectiveness of the IMA system.An optimization method based on deep reinforcement learning(DRL)is proposed for the distribution and scheduling of avionics task sets within multiple partitions is proposed.The IMA system model and task model are constructed,and the constraints of system resource and task real-time requirements are used to improve the system resource utilization as the optimization objective.The task distribution process is described as a sequential decision problem.A Markov decision model is introduced to develop a deep deterministic policy gradient(DDPG)algorithm-based IMA task distribution model and a generic distribution architecture is proposed.Policy training techniques such as state normalization and behavioral noise are introduced to improve the learning performance and training capability of the DDPG algorithm.Simulation results show that the proposed optimization algorithm starts to converge after 500 iterations,and the efficiency of distribution scheme is improved by 20.55%while satisfying the constraint requirements after 800 iterations.Compared with the traditional assignment scheme and the Actor-Critic(AC)algorithm,the proposed DDPG algorithm has significant advantages in terms of convergence ability.关键词
综合模块化航空电子系统(IMA)/任务分配及调度/深度强化学习/DDPG算法Key words
integrated modular avionics(IMA)/task allocation and scheduling/deep reinforcement learning/DDPG algorithm引用本文复制引用
赵长啸,李道俊,汪鹏辉,田毅..基于DDPG的综合化航电系统多分区任务分配优化方法[J].电讯技术,2024,64(1):58-66,9.基金项目
国家重点研发计划(2021YFB1600601) （2021YFB1600601）
天津市自然科学基金(21JCQNJC00900) （21JCQNJC00900）
