Probabilistic Inference for Fast Learning in Control

Authors:
Carl Edward Rasmussen;Marc Peter Deisenroth
Affiliations:
Department of Engineering, University of Cambridge, UK and Max Planck Institute for Biological Cybernetics, Tübingen, Germany;Department of Engineering, University of Cambridge, UK and Faculty of Informatics, Universität Karlsruhe (TH), Germany
Venue:
Recent Advances in Reinforcement Learning
Year:
2008

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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.