Few-Shot Reinforcement Learning (Glossary)

Few-shot reinforcement learning is a term used in the fields of artificial intelligence, automation and Industry 4.0. It refers to a special method in machine learning in which intelligent systems can learn independently with very little sample data. Normally, machines have to make countless attempts and collect a lot of data before they can perform tasks successfully. With few-shot reinforcement learning, however, just a few "learning occasions" are enough to achieve good results.

This is particularly useful when it is expensive, time-consuming or dangerous to carry out many training runs. Imagine, for example, a robot arm in a factory that has to learn to grip a new component correctly. Instead of practising over and over again for days, it can use Few-Shot Reinforcement Learning to apply the correct technique after just a few instructions. This saves resources and speeds up automation.

This ability to learn quickly and flexibly makes few-shot reinforcement learning a key technology for the future of industry and intelligent systems. It enables companies to react more efficiently to new tasks and changes.