To Learn or Not to Learn: A Litmus Test for Using Reinforcement Learning in Control
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arXiv
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| Format: | Preprint |
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2026
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| _version_ | 1866917403217100800 |
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| author | Schulte, Victor Eichelbeck, Michael Althoff, Matthias |
| author_facet | Schulte, Victor Eichelbeck, Michael Althoff, Matthias |
| contents | 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. In many cases, however, the choice between model-based and RL-based control is not obvious. Due to the high computational costs of training RL agents, RL-based control should be limited to cases where it is expected to yield superior results compared to model-based control. To the best of our knowledge, there exists no approach to quantify the benefit of RL-based control that does not require RL training. In this work, we present a computationally efficient, purely simulation-based litmus test predicting whether RL-based control is superior to model-based control. Our test evaluates the suitability of the given model for model-based control by analyzing the impact of model uncertainties on the control problem. For this, we use reachset-conformant model identification combined with simulation-based analysis. This is followed by a learnability evaluation of the uncertainties based on correlation analysis. This two-part analysis enables an informed decision on the suitability of RL for a control problem without training an RL agent. We apply our test to several benchmarks, demonstrating its applicability to a wide range of control problems and highlight the potential to save computational resources. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2604_11463 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | To Learn or Not to Learn: A Litmus Test for Using Reinforcement Learning in Control Schulte, Victor Eichelbeck, Michael Althoff, Matthias Systems and Control 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. In many cases, however, the choice between model-based and RL-based control is not obvious. Due to the high computational costs of training RL agents, RL-based control should be limited to cases where it is expected to yield superior results compared to model-based control. To the best of our knowledge, there exists no approach to quantify the benefit of RL-based control that does not require RL training. In this work, we present a computationally efficient, purely simulation-based litmus test predicting whether RL-based control is superior to model-based control. Our test evaluates the suitability of the given model for model-based control by analyzing the impact of model uncertainties on the control problem. For this, we use reachset-conformant model identification combined with simulation-based analysis. This is followed by a learnability evaluation of the uncertainties based on correlation analysis. This two-part analysis enables an informed decision on the suitability of RL for a control problem without training an RL agent. We apply our test to several benchmarks, demonstrating its applicability to a wide range of control problems and highlight the potential to save computational resources. |
| title | To Learn or Not to Learn: A Litmus Test for Using Reinforcement Learning in Control |
| topic | Systems and Control |
| url | https://arxiv.org/abs/2604.11463 |