Explainable Reinforcement Learning (Glossary)

Explainable reinforcement learning is primarily part of artificial intelligence as well as the fields of automation and Industry 4.0. This approach combines two important topics: Machines should learn from experience, as in reinforcement learning, and at the same time explain comprehensibly why they make certain decisions - this makes them "explainable".

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

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

Explainable reinforcement learning makes artificial intelligence more transparent and trustworthy in everyday life.