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	Department of Engineering Computational and Biological Learning Lab

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CE Rasmussen and MP Deisenroth
Probabilistic Inference for Fast Learning in Control
in Recent Advances in Reinforcement Learning
published by Springer-Verlag, LNAI series, vol. 5323, pp. 229–242, November 2008.

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.

MPI for Biological Cybernetics, Tübingen, Germany