Victor Schulte | Searcharxiv

arXiv2026

To Learn or Not to Learn: A Litmus Test for Using Reinforcement Learning in 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.

eess.SY↗

arXiv2026

Pruning for efficient deterministic global optimization over trained ReLU neural networks

Neural networks are increasingly used as surrogates in optimization problems to replace computationally expensive models. However, embedding ReLU neural networks in mathematical programs introduces significant computational challenges, particularly for deep and wide networks, due to both the formulation of the ReLU disjunction and the resulting large-scale optimization problem. This work investigates how pruning techniques can accelerate the solution of optimization problems with embedded neural networks, focusing on the mechanisms underlying the computational gains. We provide theoretical insights into how both unstructured (weight) and structured (node) pruning affect the ReLU big-M formulation, showing that pruning monotonically tightens preactivation bounds. We conduct comprehensive empirical studies across multiple network architectures using an illustrative test function and a realistic chemical process flowsheet optimization case study. Our results show that pruning achieves speedups of up to three to four orders of magnitude, with computational gains attributed to three key factors: (i) reduction in problem size, (ii) decrease in the number of integer variables, and (iii) tightening of big-M bounds. Weight pruning is particularly effective for deep, narrow networks, while node pruning performs better for shallow, wide or medium-sized networks. In the chemical engineering case study, pruning enabled convergence within seconds for problems that were otherwise intractable. We recommend adopting pruning as standard practice when developing neural network surrogates for optimization, especially for engineering applications requiring repeated optimization solves.

math.OC↗