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Reinforcement Learning Adaptive Control and Explicit Criterion Maximization
Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
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]

This paper reviews an existing algorithm for adaptive control based on explicit criterion maximization (ECM) and presents an extended version suited for reinforcement learning tasks. Furthermore, assumptions under which the algorithm convergences to a local maxima of a long term utility function are given. Such convergence theorems are very rare for reinforcement learning algorithms working with continuous state and action spaces. A number of similar algorithms, previously suggested to the reinforcement learning community, are briefly surveyed in order to give the presented algorithm a place in the field. The relations between the different algorithms is exemplified by checking their consistency on a simple problem of linear quadratic regulation (LQR).

Place, publisher, year, edition, pages
Linköping, Sweden: Linköping University, Department of Electrical Engineering , 1996. , 8 p.
Series
LiTH-ISY-R, ISSN 1400-3902 ; 1829
Keyword [en]
lReinforcement learning, Adaptive control
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:liu:diva-53328ISRN: LiTH-ISY-R-1829OAI: oai:DiVA.org:liu-53328DiVA: diva2:288584
Available from: 2010-01-21 Created: 2010-01-20 Last updated: 2014-09-15Bibliographically approved

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Knutsson, 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