Accurate and interpretable degradation formulas are critical for reliable life prediction of engineering systems. This paper presents the Reinforced Symbolic Learning framework that automatically constructs explicit system degradation equations from experimental data. By integrating symbolic regression with reinforcement learning and embedding dimensional and logical constraints, our method yields concise, physically consistent expressions with minimized manual tuning. We validate the approach on GH4169 nickel‐based superalloy under two temperature conditions (25 °C and 650 °C), where the discovered formulas achieve over 90 % of predictions within a twofold error margin. Comparative evaluations against 6 classical empirical models and 5 standard machine‐learning methods demonstrate that our framework delivers superior predictive accuracy while preserving full formula interpretability. The resulting degradation laws not only reveal underlying fatigue mechanisms but also facilitate real‐time deployment in monitoring systems, offering a generalizable path for automated, data‐driven extraction of system degradation laws.

Symbolic Regression,Reinforcement Learning,Physics-guided Optimization,Degradation Law Discovery,Interpretable Machine Learning