Real-world reinforcement learning for autonomous humanoid robot charging in a home environment
TAROS'11 Proceedings of the 12th Annual conference on Towards autonomous robotic systems
Real-world reinforcement learning for autonomous humanoid robot docking
Robotics and Autonomous Systems
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This paper describes a novel real-world reinforcement learning application: The Neuro Slot Car Racer. In addition to presenting the system and first results based on Neural Fitted Q-Iteration, a standard batch reinforcement learning technique, an extension is proposed that is capable of improving training times and results by allowing for a reduction of samples required for successful training. The Neuralgic Pattern Selection approach achieves this by applying a failure-probability function which emphasizes neuralgic parts of the state space during sampling.