Explainable Reinforcement Learning (Glossary)

Explainable Reinforcement Learning primarily belongs to Artificial Intelligence as well as the fields of automation and Industry 4.0. This approach combines two important topics: machines are meant to learn from experience, as in so-called Reinforcement Learning, and at the same time to comprehensibly explain why they make certain decisions – this makes them „explainable“.

Imagine a robot in a modern factory. This robot is constantly learning to better solve production tasks. Previously, it was often unclear why the AI behind the robot preferred certain actions. This is exactly where Explainable Reinforcement Learning comes in: it ensures that the AI's learning process and decisions are logical and understandable to humans.

This brings great advantages, for example in safety and efficiency. Because decision-makers or technicians can understand why a machine solves a task in a particular way and not another. Let's take the example of intelligent warehouse management: if the system explains why it stores goods in a specific order, employees can more easily check, intervene, and specifically improve the system.

Explainable Reinforcement Learning makes Artificial Intelligence more transparent and trustworthy in everyday life.