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Probabilistic Inference for Fast Learning in Control

2008

Conference Paper

ei


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.

Author(s): Rasmussen, CE. and Deisenroth, MP.
Book Title: EWRL 2008
Journal: Recent Advances in Reinforcement Learning: 8th European Workshop (EWRL 2008)
Pages: 229-242
Year: 2008
Month: November
Day: 0
Editors: Girgin, S. , M. Loth, R. Munos, P. Preux, D. Ryabko
Publisher: Springer

Department(s): Empirical Inference
Bibtex Type: Conference Paper (inproceedings)

DOI: 10.1007/978-3-540-89722-4_18
Event Name: 8th European Workshop on Reinforcement Learning
Event Place: Villeneuve d‘Ascq, France

Address: Berlin, Germany
Digital: 0
Language: en
Organization: Max-Planck-Gesellschaft
School: Biologische Kybernetik

Links: PDF
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BibTex

@inproceedings{5398,
  title = {Probabilistic Inference for Fast Learning in Control},
  author = {Rasmussen, CE. and Deisenroth, MP.},
  journal = {Recent Advances in Reinforcement Learning: 8th European Workshop (EWRL 2008)},
  booktitle = {EWRL 2008},
  pages = {229-242},
  editors = {Girgin, S. , M. Loth, R. Munos, P. Preux, D. Ryabko},
  publisher = {Springer},
  organization = {Max-Planck-Gesellschaft},
  school = {Biologische Kybernetik},
  address = {Berlin, Germany},
  month = nov,
  year = {2008},
  month_numeric = {11}
}