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FBIT
2007
IEEE

Learning to Drive a Real Car in 20 Minutes

12 years 2 months ago
Learning to Drive a Real Car in 20 Minutes
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.
Martin Riedmiller, Michael Montemerlo, Hendrik Dah
Added 02 Jun 2010
Updated 02 Jun 2010
Type Conference
Year 2007
Where FBIT
Authors Martin Riedmiller, Michael Montemerlo, Hendrik Dahlkamp
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