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@INBOOK{Rasmussen2008,
  chapter = {Probabilistic {Inference for Fast Learning in Control}},
  pages = {229--242},
  title = {Recent {Advances in Reinforcement Learning}},
  publisher = {Springer-Verlag},
  year = {2008},
  editor = {S. Girgin and M. Loth and R. Munos and P. Preux and D. Ryabko},
  author = {Carl E. Rasmussen and Marc P. Deisenroth},
  volume = {5323},
  series = {Lecture Notes in Computer Science},
  type = {Lecture Notes in Artificial Intelligence},
  month = {November},
  abstract = {We provide a novel framework for very fast model-based reinforcement
	
	learning in continuous state and action spaces. 
	
	The framework requires probabilistic models that explicitly
	
	characterize their levels of confidence. Within this framework, we
	use
	
	flexible, non-parametric models to describe the world based on
	
	previously collected experience.
	
	We demonstrate learning on the cart-pole problem in a setting where
	we provide very
	
	limited prior knowledge about the task. Learning progresses rapidly,
	
	and a good policy is found after only a hand-full of iterations.},
}