# Evolutionary Warm-Starts for Reinforcement Learning in Industrial Continuous Control

arXiv: https://arxiv.org/abs/2603.26750 · HTML: https://mlsanity.com/paper/2603.26750/

## 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.

## Placed in

- Deep reinforcement learning methods / [RL for energy, networks and operations](https://mlsanity.com/deeprl/rl-for-energy-networks-and-operations/)

## Builds on (most cited first, 4 of 4)

- [Evolution Strategies as a Scalable Alternative to Reinforcement Learning](https://mlsanity.com/paper/1703.03864/) (2017)
- [A Benchmark Environment Motivated by Industrial Control Problems](https://mlsanity.com/paper/1709.09480/) (2017)
- [SortingEnv: An Extendable RL-Environment for an Industrial Sorting Process](https://mlsanity.com/paper/2503.10466/) (2025)
- [Leveraging Genetic Algorithms for Efficient Demonstration Generation in Real-World Reinforcement Learning Environments](https://mlsanity.com/paper/2507.00762/) (2025)

## 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.