Practical Guidelines to Train Reinforcement Learning Based Control of Electrical Drives

Abstract:
With the recent advancements made in Artificial Intelligence, it is possible that Reinforcement Learning based data driven methods can become a next generation technology to control electrical drives instead of the classical model-based techniques. However, providing the correct setup and hyperparameters for training the agent is usually not evident, so the paper aims to lay some practical guidelines on how to properly set the environmental parameters during training. These recommendations are backed up by a detailed comparison where the effect on the performance of each environmental aspect is evaluated.