Probabilistic Inference for Fast Learning in Control

Rasmussen, CE; Deisenroth, MP

doi:10.1007/978-3-540-89722-4_18

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Probabilistic Inference for Fast Learning in Control

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Rasmussen, CE
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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https://link.springer.com/content/pdf/10.1007%2F978-3-540-89722-4_18.pdf
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Citation

Rasmussen, C., & Deisenroth, M. (2008). Probabilistic Inference for Fast Learning in Control. In S. Girgin, M. Loth, R. Munos, P. Preux, & D. Ryabko (Eds.), Recent Advances in Reinforcement Learning: 8th European Workshop, EWRL 2008, Villeneuve d’Ascq, France, June 30-July 3, 2008 (pp. 229-242). Berlin, Germany: Springer.

Cite as: https://hdl.handle.net/11858/00-001M-0000-0013-C66B-7

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.