Conference Proceedings
Learning to drive a real car in 20 minutes
Martin Riedmiller, Mike Montemerlo, Hendrik Dahlkamp, D Howard (ed.), PK Rhee (ed.), S Halgamuge (ed.), SJ Yoo (ed.)
PROCEEDINGS OF THE FRONTIERS IN THE CONVERGENCE OF BIOSCIENCE AND INFORMATION TECHNOLOGIES | IEEE COMPUTER SOC | Published : 2007
DOI: 10.1109/FBIT.2007.37
Abstract
The paper describes our first experiments on Reinforcement Learning to steer a real robot car. The applied method, Neural Fitted Q Iteration (NFQ) is purely data-driven based on data directly collected from real-life experiments, i.e. no transition model and no simulation is used. The RL approach is based on learning a neural Q value function, which means that no prior selection of the structure of the control law is required. We demonstrate, that the controller is able to learn a steering task in less than 20 minutes directly on the real car. We consider this as an important step towards the competitive application of neural Q function based RL methods in real-life environments.