Reinforcement learning (RL) is still rarely applied in industrial control, partly due to the difficulty of training reliable agents for real-world conditions. This work investigates how evolution strategies can support RL in such settings by introducing a continuous-control adaptation of an industrial sorting benchmark. The CMA-ES algorithm is used to generate high-quality demonstrations that warm-start RL agents. Results show that CMA-ES-guided initialization significantly improves stability and performance. Furthermore, the demonstration trajectories generated with the CMA-ES provide a strong oracle reference performance level, which is of interest in its own right. The study delivers a focused proof of concept for hybrid evolutionary-RL approaches and a basis for future, more complex industrial applications.
@article{arxiv.2603.26750,
title = {Evolutionary Warm-Starts for Reinforcement Learning in Industrial Continuous Control},
author = {Tom Maus and Stephan Frank and Tobias Glasmachers},
journal= {arXiv preprint arXiv:2603.26750},
year = {2026}
}