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  Reinforcement Learning and the Bayesian Control Rule

Ortega, P., Braun, D., & Godsill, S. (2011). Reinforcement Learning and the Bayesian Control Rule. In Artificial General Intelligence (pp. 281-285). Berlin, Germany: Springer.

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 Creators:
Ortega, PA1, Author           
Braun, DA1, Author           
Godsill, S, Author
Schmidhuber, Editor
J., Editor
Thórisson, K.R., Editor
Looks, M., Editor
Affiliations:
1Research Group Sensorimotor Learning and Decision-Making, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497809              

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 Abstract: We present an actor-critic scheme for reinforcement learning in complex domains. The main contribution is to show that planning and I/O dynamics can be separated such that an intractable planning problem reduces to a simple multi-armed bandit problem, where each lever stands for a potentially arbitrarily complex policy. Furthermore, we use the Bayesian control rule to construct an adaptive bandit player that is universal with respect to a given class of optimal bandit players, thus indirectly constructing an adaptive agent that is universal with respect to a given class of policies.

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 Dates: 2011-08
 Publication Status: Issued
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: ISBN: 978-3-642-22886-5
URI: http://agi-conf.org/2011/
DOI: 10.1007/978-3-642-22887-2_30
BibTex Citekey: OrtegaBG2011
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Title: Fourth International Conference on Artificial General Intelligence (AGI 2011)
Place of Event: Mountain View, CA, USA
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Title: Artificial General Intelligence
Source Genre: Proceedings
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Publ. Info: Berlin, Germany : Springer
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 281 - 285 Identifier: -