Reinforcement learning for robot soccer
Autonomous Robots
Anticipatory Driving for a Robot-Car Based on Supervised Learning
Anticipatory Behavior in Adaptive Learning Systems
Reinforcement learning algorithms with function approximation: Recent advances and applications
Information Sciences: an International Journal
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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.