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  Reinforcement Learning and the Bayesian Control Rule

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

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Item Permalink: https://hdl.handle.net/11858/00-001M-0000-0013-BADC-6 Version Permalink: https://hdl.handle.net/21.11116/0000-0002-0691-3
Genre: Conference Paper

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https://link.springer.com/content/pdf/10.1007%2F978-3-642-22887-2_30.pdf (Publisher version)
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 Creators:
Ortega, PA1, Author           
Braun, DA1, Author           
Affiliations:
1Department of Engineering, University of Cambridge, ou_persistent22              

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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: -
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 Rev. Type: -
 Identifiers: 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
Start-/End Date: 2011-08-03 - 2011-08-06

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Title: Artificial General Intelligence
Source Genre: Proceedings
 Creator(s):
Godsill, S, Editor
Schmidhuber, J, Editor
Thórisson, KR, Editor
Looks, M, Editor
Affiliations:
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Publ. Info: Berlin, Germany : Springer
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 281 - 285 Identifier: ISBN: 978-3-642-22886-5

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Title: Lecture Notes in Computer Science
Source Genre: Series
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Pages: - Volume / Issue: 6830 Sequence Number: - Start / End Page: - Identifier: -