arXiv:2604.11463v2 Announce Type: replace Abstract: Reinforcement learning (RL) can be a powerful alternative to classical control methods when standard model-based control is insufficient, e.g., when deriving a suitable model is intractable or impossible. However, in many cases, the choice between model-based and RL-based control is not obvious. Because training RL agents is computationally expensive, RL should be limited to cases where it is expected to outperform model-based control. To the best of our knowledge, there exists no approach to automatically screen out control problems before RL training for which RL is not expected to yield any benefit. In this work, we take the first step towards such an automated screening test. We present a test that identifies problems for which RL-based control is not expected to be superior in two steps. Only control problems that pass both steps should be considered for RL. Part I evaluates the suitability of a provided model for model-based control by analyzing the impact of its uncertainties on the control problem. For this, we use reachset-conformant system identification combined with simulation-based analysis. If the uncertainties are found to be relevant in Part I, Part II then evaluates their learnability using a statistically calibrated dependence analysis. We apply our screening test to several benchmarks, reliably identifying control problems for which RL-based control does not measurably improve upon model-based control, thereby achieving a speed-up of up to $21\times$ compared to trying RL.