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Probabilistic Inference for Fast Learning in Control

MPG-Autoren
http://pubman.mpdl.mpg.de/cone/persons/resource/persons84156

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

http://pubman.mpdl.mpg.de/cone/persons/resource/persons84381

Deisenroth,  MP
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Zitation

Rasmussen, C., & Deisenroth, M. (2008). Probabilistic Inference for Fast Learning in Control. Recent Advances in Reinforcement Learning: 8th European Workshop (EWRL 2008), 229-242.


Zitierlink: http://hdl.handle.net/11858/00-001M-0000-0013-C66B-7
Zusammenfassung
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.