Using k-means for clustering in complex automotive production systems to support a Q-learning-system

This work shows the application of k-means clustering to reduce the state space complexity for a q-leaning algorithm in supply networks of serial production systems. An adequate clustering function is introduced and based on several scenarios the results of the clustering are validated with respect to their usability for the q-learning system. In addition, runtime and scalability aspects are evaluated for those scenarios.