Evolutionary Warm-Starts for Reinforcement Learning in Industrial Continuous Control

Tom Maus; Stephan Frank; Tobias Glasmachers

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arXiv · 2603.26750

Evolutionary Warm-Starts for Reinforcement Learning in Industrial Continuous Control

Tom Maus·Stephan Frank·Tobias Glasmachers

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Abstract

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.

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Tom Maus, Stephan Frank, Tobias Glasmachers. 2026-03-23. Evolutionary Warm-Starts for Reinforcement Learning in Industrial Continuous Control. https://arxiv.org/abs/2603.26750

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