The Neuro Slot Car Racer: Reinforcement Learning in a Real World Setting

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.

• Type: Journal
• Year: 2009
• Where: ICMLA
• Authors: Tim C. Kietzmann, Martin Riedmiller
• Keywords: ICMLA 2009, Machine Learning, Neural Fitted Q-iteration, Neuro Slot Car, Standard Batch Reinforcement