Prioritized Replay Deep Reinforcement Learning for Preventive Maintenance in a Multi‐Unit k‐out‐of‐n: G System

ABSTRACT This paper investigates the dynamic maintenance problem of the multi‐unit k‐out‐of‐n: G system. First, the real‐time dynamic maintenance decision problem is modeled by a sequential decision‐making framework aimed at a k‐out‐of‐n: G system subject to stochastic failures. Second, a maintenance decision agent is established by efficiently integrating a customized deep reinforcement learning (DRL) method, in which there are several important improvements: (i) the dueling deep neural network is established to optimize the maintenance decision process by independently learning the state‐value function and action‐advantage function; (ii) the non‐uniform prioritized experience replay technique is adopted to enhance the training efficiency of the decision‐making agent; (iii) the weighted double Q network technique is developed to alleviate the estimation error of the maintenance decision agent, and enhance the system reliability through the weighted combination of dueling neural network estimators. Finally, the effectiveness is validated by carrying out numerical experiments, and the superiority is demonstrated by comparing the proposed method with several popular DRL algorithms.