mlsanityData to 14 Oct 2026

Evolutionary Warm-Starts for Reinforcement Learning in Industrial Continuous Control

arXiv 2603.26750 ↗First posted 23 Mar 2026cs.NE cs.LGcited by 0

On the map: Deep reinforcement learning methods → RL for energy, networks and operations

Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in RL for energy, networks and operations, one of 15 directions in Deep reinforcement learning methods. Builds on 4 of its 9 references that are in the map. First posted on arXiv 23 Mar 2026.

Builds on

4 of 9 references.

References · 4

  1. 2017Evolution Strategies as a Scalable Alternative to Reinforcement Learningcited by 574
  2. 2017A Benchmark Environment Motivated by Industrial Control Problemscited by 31
  3. 2025SortingEnv: An Extendable RL-Environment for an Industrial Sorting Processcited by 3
  4. 2025Leveraging Genetic Algorithms for Efficient Demonstration Generation in Real-World Reinforcement Learning Environmentscited by 3

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