Reinforcement learning (RL) is emerging as a key solution for controlling robots in warehouses. These AI models enable robots to learn autonomously and optimize their operations depending on the results they have achieved, lenabling them to respond more efficiently to challenges.

The main difference in reinforcement learning compared to supervised and unsupervised learning is that training data does not need to be provided as an external dataset. These are generated automatically while the robot is being trained. The precondition for this is a simulated environment that is used to train it on safety and time.

In a dynamic environment such as a warehouse, where conditions and requirements may change constantly, this adaptability is essential. Reinforcement learning enables robots to learn from interaction with the environment and thereby continuously optimize performance over time. This contributes to reducing errors as well as increasing efficiency and autonomy.

To sum up, by its ability to self-optimize, adapt to dynamic environments, and handle complex tasks, reinforcement learning represents a cutting-edge solution for controlling robots in warehouses.