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URN: urn:nbn:de:0030-drops-24265
URL: http://drops.dagstuhl.de/opus/volltexte/2010/2426/

Gabel, Thomas

Cooperative Multi-Agent Systems from the Reinforcement Learning Perspective -- Challenges, Algorithms, and an Application

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Abstract

Reinforcement Learning has established as a framework that allows an autonomous agent for automatically acquiring -- in a trial and error-based manner -- a behavior policy based on a specification of the desired behavior of the system. In a multi-agent system, however, the decentralization of the control and observation of the system among independent agents has a significant impact on learning and it complexity. In this survey talk, we briefly review the foundations of single-agent reinforcement learning, point to the merits and challenges when applied in a multi-agent setting, and illustrate its potential in the context of an application from the field of manufacturing control and scheduling.

BibTeX - Entry

@InProceedings{gabel:DSP:2010:2426,
  author =	{Thomas Gabel},
  title =	{Cooperative Multi-Agent Systems from the Reinforcement Learning Perspective -- Challenges, Algorithms, and an Application},
  booktitle =	{Algorithmic Methods for Distributed Cooperative Systems},
  year =	{2010},
  editor =	{S{\'a}ndor Fekete and Stefan Fischer and Martin Riedmiller and Suri Subhash},
  number =	{09371},
  series =	{Dagstuhl Seminar Proceedings},
  ISSN =	{1862-4405},
  publisher =	{Schloss Dagstuhl - Leibniz-Zentrum fuer Informatik, Germany},
  address =	{Dagstuhl, Germany},
  URL =		{http://drops.dagstuhl.de/opus/volltexte/2010/2426},
  annote =	{Keywords: Multi-agent reinforcement learning, decentralized control, job-shop scheduling}
}

Keywords: Multi-agent reinforcement learning, decentralized control, job-shop scheduling
Seminar: 09371 - Algorithmic Methods for Distributed Cooperative Systems
Issue date: 2010
Date of publication: 22.04.2010


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