Abstract：3. Aeronautics Computing Technology Research Institute， Xi’an 710119， Shaanxi， China）
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.
