Reinforcement Learning (RL) is a term from the fields of Artificial Intelligence, automation, and Industry 4.0. In short, Reinforcement Learning describes a method by which computer models learn through trial and error to perform specific tasks ever more effectively \u2013 almost like a human who becomes smarter through experience.<\/p>\n
In reinforcement learning (RL), an artificial intelligence is given a task. For example, a robot in a factory is to sort packages. At first, the robot doesn't know how to do this correctly. Every time it does something right (e.g. puts a package in the right place), it receives a \u201ereward\u201c, usually in the form of points. If it does something wrong, it gets negative points. Gradually, the robot learns what works and what doesn't.<\/p>\n
This principle helps, for example, to make robots work very efficiently in production because they can improve their strategies independently. Reinforcement Learning (RL) is also used in digital assistants, which, for example, make recommendations, in order to adapt better and better to the needs of users.<\/p>","protected":false},"excerpt":{"rendered":"