Evolutionary Warm-Starts for Reinforcement Learning in Industrial Continuous Control
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...
| Authors | Tom Maus, Stephan Frank, Tobias Glasmachers |
|---|---|
| Primary category | cs.NE |
| Published | 23 March 2026 |
| Last updated at source | 23 March 2026 |
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