Scalable Reinforcement Learning is a term from the fields of Artificial Intelligence, automation, and Industry 4.0. Reinforcement Learning, in German \u201ebest\u00e4rkendes Lernen\u201c (literally \u201ereinforcing learning\u201c), is a method whereby a computer programme learns to solve problems independently through trial and error, and feedback. \"Scalable\" means that this learning method can be used not only for small tasks but also for very large or complex applications.<\/p>\n
Imagine, for example, a modern production line in a factory. At the start, an Artificial Intelligence (AI) might only be able to learn to control a single machine optimally. However, with scalable reinforcement learning, it's possible for the AI to independently control and improve more and more machines, processes, and even the entire production chain over time \u2013 all without having to completely relearn every single time.<\/p>\n
This makes scalable reinforcement learning particularly interesting for companies, as it helps to make processes more efficient, flexible, and cost-effective \u2013 even if requirements or production sizes change. Therefore, anyone looking for future-proof and adaptable automation or AI solutions can hardly avoid scalable reinforcement learning.<\/p>","protected":false},"excerpt":{"rendered":"