Evolutionary Warm-Starts for Reinforcement Learning in Industrial Continuous Control

• URL: http://arxiv.org/abs/2603.26750v1
• Date: Mon, 23 Mar 2026 11:17:23 GMT
• Title: Evolutionary Warm-Starts for Reinforcement Learning in Industrial Continuous Control
• Abstract summary: Reinforcement learning (RL) is still rarely applied in industrial control, partly due to the difficulty of training reliable agents for real-world conditions.<n>This work investigates how evolution strategies can support RL in such settings by introducing a continuous-control adaptation of an industrial sorting benchmark.<n>The CMA-ES algorithm is used to generate high-quality demonstrations that warm-start RL agents.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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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