Optimization of Nuclear Power Accident Diagnosis Procedures Based on SAC Reinforcement Learning

This paper proposes an optimization method for nuclear accident diagnosis procedures based on the Soft Actor-Critic (SAC) reinforcement learning model. Using a decision tree model as the foundation to optimize the judgment strategy of accident detection procedures, which significantly improves the performance of accident detection while maintaining the interpretability of the decision model. The model employs SAC as the reinforcement learning algorithm, which defines the state as a combination of current operating data and historical data, sets the actions as the adjustment of the decision threshold of diagnostic procedures, and reflects the accuracy of diagnosis through the returns. With the help of SAC algorithm, the system constantly adjusts the threshold to optimize the strategy to obtain the best diagnosis effect. In a simulated Main Steam Line Break (MSLB) accident scenario, the model can better adapt to and comprehend complex high-dimensional data, find the optimal control strategy under specific performance indicators, and the accuracy is steadily approaching 1. The proposed method significantly reduces the false positive rate, and it not only detects nuclear power accidents more accurately, but also shows excellent results in reducing false alarms, thus improving the safety of nuclear power operation.