Applications of deep reinforcement learning in nuclear energy: A review

Abstract

In recent years, Deep reinforcement learning (DRL), as an important branch of artificial intelligence (AI), has been widely used in physics and engineering domains. It combines the perceptual advantages of deep learning (DL) and the decision-making advantages of reinforcement learning (RL), and is very suitable for solving the “perception-decision” problem with high-dimensional and nonlinear characteristics. In this paper, firstly, the algorithm principle, mainstream framework, characteristics and advantages of DRL are summarized. Secondly, the application research status of DRL in other energy fields is reviewed, which provides reference for the possible impact and future research direction in the field of nuclear energy. Thirdly, the main research directions of DRL in the field of nuclear energy are summarized and commented, and the application architecture and advantages of DRL are illustrated through specific application cases. Finally, the advantages, limitations and future development direction of DRL in the field of nuclear energy are discussed. The goal of this review is to provide an understanding of DRL capabilities along with state-of-the-art applications in nuclear energy to researchers wishing to address new problems with these methods.