An experimental study on the application of reinforcement learning in injection molding in the spirit of Industry 4.0

The use of reinforcement learning in the injection molding process is a little-researched area in the era of Industry 4.0. The use of a smart decision-making algorithm is necessary for such a complex production method. Therefore, our research aims to extend the knowledge of the practical use of reinforcement learning in injection molding. In our study, we examined the effect of the parameters of the Actor-Critic algorithm to give a broader picture of the learning process. In addition, we show how to use simulation data, as prior knowledge, to set up the injection molding process for the production of an unknown part. • Reinforcement learning can be used to improve injection molding technology. • Actor-critic algorithms are helpful in setting up an injection molding machine. • The use of prior knowledge is necessary for self-learning machines. • Injection molding simulations can be used as prior knowledge for self-learning. • Our algorithm can compete with a professional through relevant prior knowledge.