Most industrial processes still run on the same foundation:

- Hard-coded logic in PLCs that follows predefined rules.

- The intuition of process and plant engineers, built from years of experience.

This combination has powered industry for decades, but it has limits.

When the challenge involves many interacting variables, unknown relationships, and non-linear effects, traditional control starts to strain.

Why?

Because fixed rules can’t adapt fast enough to changing conditions, and even the best human intuition can only process so much complexity at once.

Instead of relying on fixed instructions, RL agents learn directly from real-time feedback.

They can:

✅ Adapt continuously to new conditions.

✅ Handle high-dimensional problems with countless variables.

✅ Uncover novel, more efficient strategies that humans might overlook.

The result?

An optimization layer that works alongside your existing control system, making it smarter, more adaptive, and capable of delivering gains where complexity used to be a roadblock.

In this episode of the AI in Manufacturing podcast, I sat down with Dr. Kyrill Schmid, the Lead AI Engineer at MaibornWolff GmbH, to discuss the application of reinforcement learning agents for optimizing industrial plants.

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