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Reinforcement Learning Trees
n/a.
Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.ORCID iD: 0000-0002-9267-2191
Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.ORCID iD: 0000-0002-9091-4724
1996 (English)Report (Other academic)
Abstract [en]

Two new reinforcement learning algorithms are presented. Both use a binary tree to store simple local models in the leaf nodes and coarser global models towards the root. It is demonstrated that a meaningful partitioning into local models can only be accomplished in a fused space consisting of both input and output. The first algorithm uses a batch like statistic procedure to estimate the reward functions in the fused space. The second one uses channel coding to represent the output- and input vectors allowing a simple iterative algorithm based on competing subsystems. The behaviors of both algorithms are illustrated in a preliminary experiment.

Place, publisher, year, edition, pages
Linköping, Sweden: Linköping University, Department of Electrical Engineering , 1996. , 8 p.
Series
LiTH-ISY-R, ISSN 1400-3902 ; 1828
Keyword [en]
Learning algorithms, Autonomous systems
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:liu:diva-53411ISRN: LiTH-ISY-R-1828OAI: oai:DiVA.org:liu-53411DiVA: diva2:288282
Available from: 2010-01-20 Created: 2010-01-20 Last updated: 2014-10-08Bibliographically approved

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Type fulltextMimetype application/pdf

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Borga, MagnusKnutsson, Hans

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CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • oxford
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf