Deep Reinforcement Learning (DRL) (Glossary)

Deep Reinforcement Learning (DRL) belongs to the category of Artificial Intelligence and also finds its place in the fields of automation and industry and Industry 4.0. It is a modern method for training machines to solve tasks independently through trial and error and to continuously improve.

Unlike conventional Artificial Intelligence, where fixed rules are predetermined, a Deep Reinforcement Learning (DRL) system learns based on experience. The machine is given a task and tries out various actions. For correct decisions, it receives a reward – for mistakes, a „punishment.“ In this way, the system gradually finds the best way to master a task.

A vivid example: In a modern factory, a robot arm controls the assembly of car parts. Initially, the robot makes many mistakes. However, with the help of Deep Reinforcement Learning (DRL), it learns with every movement and becomes increasingly precise and faster. In the end, the robot works autonomously and reliably.

So, Deep Reinforcement Learning (DRL) helps companies automate processes and significantly increase their efficiency.