Learning to Drive a Real Car in 20 Minutes

摘要

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.