Useability Evaluation of Reinforcement Learning Toolboxes for Electrical Drives

Abstract

The current direction of development predicts that Reinforcement Learning based data driven control methods can become a next generation technology to control electrical drives instead of the classical model-based techniques. The paper aims to evaluate toolboxes that can be used to train agents for control approaches. The paper helps lay the theoretical bases and provides guidelines for using these toolboxes via a case study. This is done to highlight each toolbox’s key aspects and workflow patterns, shifting the comparison to useability and peak-performance.