Intelligent Parameter Tuning Strategy for Nuclear Power Simulation Systems Based on Soft Actor-Critic

The effective tuning of extensive, high-dimensional parameter sets within nuclear power simulation systems presents considerable challenges for conventional, experience-based methodologies. Such approaches often exhibit limitations including slow convergence rates, susceptibility to local optima, and inadequate adaptability across diverse scenarios. This study introduces an automated parameter tuning strategy employing the Soft Actor-Critic (SAC) reinforcement learning approach. A SAC-based agent is formulated, where state representations, action spaces, and reward functions are specifically defined to tune parameters under predefined constraints. This facilitates the automated adjustment of complex parameter configurations. Comparative experimental evaluations indicate that the proposed SAC method reaches a solution quality comparable to that of the established baseline techniques while producing markedly more consistent results across independent runs, offering a reliable and repeatable methodology for parameter tuning in this domain.