Speaker
Description
Recently reinforcement learning (RL) methods have been used to solve a wide range of complex control problems. One reason for the increased interest in applying RL methods to control problems is due to their ability to generate control laws solely through interaction with the plant. The control law can be trained directly out of interaction with the real-world system or by using a simulation model. Training the controller in simulation is a promising approach, because it is fast, scalable, safe and has shown positive results. Therefore, RL methods are increasingly applied in motion control of aerial, marine and ground vehicles.
In this talk we want to give insights into the application of model free RL to control problems. We will showcase the RL design process through application to the path following problem of an over-actuated robotic vehicle. Moreover, we will discuss potential benefits as well as unsolved problems when applying RL to control problems
