空天防御2024，Vol.7Issue(1)：63-70,8.
基于深度强化学习的综合电子系统重构方法
Deep Reinforcement Learning-Based Reconfiguration Method for Integrated Electronic Systems
摘要
Abstract
Reconfiguration is widely used by integrated electronic systems to enhance its fault tolerance and stability.It involves transforming a system from a faulty state to a normal state using a series migration actions based on a pre-defined reconfiguration blueprint after fault occurred.Considering the existing functional diversification and structural complexity of integrated electronic systems,it is crucial to enhance the fault tolerance and stability of the system.However,the current manual reconfiguration and conventional reconfiguration algorithms,two methods for designing reconfiguration configuration blueprints,are challenging to the fault tolerance and stability requirements of integrated electronic systems.This study has integrated the deep reinforcement learning algorithm to determine the reconfiguration blueprint model for the integrated electronic system fault situation and has proposed the Prioritized Experience Playback-based Competitive Deep Q-Network algorithm(PEP_DDQN).Utilizing the prioritized experience playback mechanism and SUMTREE's batch sample extraction technique,the proposed algorithm has built a competitive deep Q-network reconstruction algorithm based on deep reinforcement learning.Experiment results demonstrated that the PEP_DDQN method can outperform traditional reinforcement learning Q-Learning and DQN algorithms in generating higher-quality blueprints.It also exhibits better convergence performance and solution speed.关键词
综合模块化航空电子系统/智能重构/深度强化学习/DQN算法Key words
integrated modular avionics system/intelligent reconfiguration/deep reinforcement learning/DQN algorithm分类
航空航天引用本文复制引用
马驰,张国群,孙俊格,吕广喆,张涛..基于深度强化学习的综合电子系统重构方法[J].空天防御,2024,7(1):63-70,8.基金项目
航空科学基金项目(20185853038,201907053004) （20185853038,201907053004）
上海航天科技创新基金项目(SAST2021-054) （SAST2021-054）
