A look at the origins of reinforcement learning

Reinforcement learning is by no means new - on the contrary: its roots go back to the psychology of the early 20th century. Originally, animals were observed in mazes in order to understand how they learn through rewards. This principle was later transferred to machines. Today, RL is the basis of many modern AI technologies, including the latest language models such as DeepSeek.

What makes reinforcement learning special?

While classic machine learning methods often work with predefined data sets, RL operates in a dynamic environment. An RL agent interacts with its environment, learns through trial and error and optimizes its actions based on the rewards it receives. This concept can be used for a wide range of applications - from robotics and autonomous vehicles to industrial processes.

The use of reinforcement learning in industry

Despite its potential, RL remains a challenge in industry. Companies often find it difficult to recognize the added value for their processes. “Many industrial partners come to us with an optimization problem and think they need RL. But it often turns out that classic optimization methods are more effective,” says Koutnik.

An RL problem arises when an environment is constantly changing - for example, when machines in a factory wear out or production parameters vary. Here, RL can help to adapt and make optimal decisions.

The challenge of data

A major problem for industrial use is data availability. In contrast to supervised learning methods, where large data sets can be collected in advance, an RL agent must build up its knowledge through interaction. However, this can be problematic in a real factory. “You can't just let the RL agent loose to learn through trial and error because that would cause production losses or damage,” explains Koutnik. One solution is to use simulations and digital twins as a training environment.

Automation through RL: the dream of the 'dark factory'

A long-term goal of many industries is fully automated production - the so-called 'dark factory'. RL could play a decisive role here. Continuous learning could enable machines to independently optimize processes and adapt to new conditions.

But Kutnik remains realistic: “RL is not a miracle cure. It requires careful modelling, robust algorithms and close integration with classic optimization methods.”

Misunderstandings about reinforcement learning

While RL is hyped in the scientific and AI community, there are many misconceptions. Koutnik names three statements that he can no longer hear:

1. “Agent systems are the future ‘ - The term ’agent” is often overused. Originally it referred to distributed AI systems, but today it is often used for simple programs that interact with large language models.

2. "There is a magic tweak ” - Many talks suggest that there are simple parameters that magically improve AI solutions. In fact, RL is highly complex and requires in-depth modeling.

3. "Every industry needs RL ” - While RL is great for some problems, there are many areas where classical methods are more efficient and easier to implement.

The future of reinforcement learning in the industry

According to Koutnik, there are two main directions of development for RL: firstly, greater integration into industrial processes in order to demonstrate the benefits more clearly to companies. Secondly, the further development of algorithms, particularly through the integration of modern methods such as transformer models.

Despite the challenges, RL remains one of the most exciting areas of artificial intelligence. “We are only at the beginning,” says Koutnik. “The combination of RL with modern AI technologies will enable revolutionary developments in the coming years.”