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  Parameter-exploring policy gradients

Sehnke, F., Osendorfer, C., Rückstiess, T., Graves, A., Peters, J., & Schmidhuber, J. (2010). Parameter-exploring policy gradients. Neural networks, 21(4), 551-559. doi:10.1016/j.neunet.2009.12.004.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0013-C026-2 Version Permalink: http://hdl.handle.net/21.11116/0000-0002-76A7-D
Genre: Journal Article

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 Creators:
Sehnke, F, Author
Osendorfer, C, Author
Rückstiess, T, Author
Graves, A, Author
Peters, J.1, 2, Author              
Schmidhuber, J, Author
Affiliations:
1Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795              
2Max Planck Institute for Biological Cybernetics, Max Planck Society, Spemannstrasse 38, 72076 Tübingen, DE, ou_1497794              

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 Abstract: We present a model-free reinforcement learning method for partially observable Markov decision problems. Our method estimates a likelihood gradient by sampling directly in parameter space, which leads to lower variance gradient estimates than obtained by regular policy gradient methods. We show that for several complex control tasks, including robust standing with a humanoid robot, this method outperforms well-known algorithms from the fields of standard policy gradients, finite difference methods and population based heuristics. We also show that the improvement is largest when the parameter samples are drawn symmetrically. Lastly we analyse the importance of the individual components of our method by incrementally incorporating them into the other algorithms, and measuring the gain in performance after each step.

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 Dates: 2010-05
 Publication Status: Published in print
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: DOI: 10.1016/j.neunet.2009.12.004
BibTex Citekey: 6154
 Degree: -

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Title: Neural networks
Source Genre: Journal
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Publ. Info: New York : Pergamon
Pages: - Volume / Issue: 21 (4) Sequence Number: - Start / End Page: 551 - 559 Identifier: ISSN: 0893-6080
CoNE: https://pure.mpg.de/cone/journals/resource/954925558496