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  Relative Entropy Policy Search

Peters, J., Mülling, K., & Altun, Y. (2010). Relative Entropy Policy Search. In Twenty-Fourth National Conference on Artificial Intelligence (AAAI-10) (pp. 1607-1612). Menlo Park, CA, USA: AAAI Press.

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Item Permalink: https://hdl.handle.net/11858/00-001M-0000-0013-BF48-A Version Permalink: https://hdl.handle.net/11858/00-001M-0000-0013-BF49-8
Genre: Conference Paper

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 Creators:
Peters, J1, 2, Author           
Mülling, K1, Author           
Altun, Y1, Author           
Fox D. Poole, M., Editor
Affiliations:
1Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795              
2Dept. Empirical Inference, Max Planck Institute for Intelligent Systems, Max Planck Society, ou_1497647              

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 Abstract: Policy search is a successful approach to reinforcement learning. However, policy improvements often result in the loss of information. Hence, it has been marred by premature convergence and implausible solutions. As first suggested in the context of covariant policy gradients (Bagnell and Schneider 2003), many of these problems may be addressed by constraining the information loss. In this paper, we continue this path of reasoning and suggest the Relative Entropy Policy Search (REPS) method. The resulting method differs significantly from previous policy gradient approaches and yields an exact update step. It works well on typical reinforcement learning benchmark problems.

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 Dates: 2010-07
 Publication Status: Issued
 Pages: -
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 Rev. Type: -
 Identifiers: ISBN: 978-1-577-35463-5
URI: http://www.aaai.org/Conferences/AAAI/aaai10.php
BibTex Citekey: 6439
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Title: Twenty-Fourth National Conference on Artificial Intelligence (AAAI-10)
Place of Event: Atlanta, GA, USA
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Title: Twenty-Fourth National Conference on Artificial Intelligence (AAAI-10)
Source Genre: Proceedings
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Publ. Info: Menlo Park, CA, USA : AAAI Press
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 1607 - 1612 Identifier: -