PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Probabilistic Inference for Fast Learning in Control
Carl Edward Rasmussen and Marc Deisenroth
In: Recent Advances in Reinforcement Learning Lecture Notes in Computer Science , 5323 . (2008) Springer Verlag , Germany , pp. 229-242. ISBN 9783540897217

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.

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EPrint Type:Book Section
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Learning/Statistics & Optimisation
Theory & Algorithms
ID Code:4282
Deposited By:Marc Deisenroth
Deposited On:06 March 2009