Learning to Control Direct Current Motor for Steering in Real Time via Reinforcement Learning

Learning to quickly control a complex dynamical system (continuous and non-linear) in the presence of disturbances and uncertainties is often desired in many industrial and robotic applications. However, techniques to accomplish this task usually rely on a mathematical system model, which is often insufficient to anticipate the effects of time-varying and interrelated sources of non-linearities. Furthermore, most model-free approaches that have been successful at this task rely on massive interactions with the system (usually in a simulation) and are trained in specialized hardware to fit a highly parameterized controller. In this work, we learn to control one such dynamical system (steering position control of a DC motor) using the sample efficient technique named Neural fitted Q. Using data collected from hardware interactions in the real world, we additionally build a simulator to experiment with a wide range of parameters and learning strategies. Using the parameters found in simulation, we successfully learn an effective control policy in one minute and 53 seconds on a simulation and in 10 minutes and 35 seconds on a physical system.