License: Creative Commons Attribution 4.0 International license (CC BY 4.0)
When quoting this document, please refer to the following
DOI: 10.4230/DagSemProc.09371.2
URN: urn:nbn:de:0030-drops-24265
URL: http://dagstuhl.sunsite.rwth-aachen.de/volltexte/2010/2426/
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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:DagSemProc.09371.2,
  author =	{Gabel, Thomas},
  title =	{{Cooperative Multi-Agent Systems from the Reinforcement Learning Perspective – Challenges, Algorithms, and an Application}},
  booktitle =	{Algorithmic Methods for Distributed Cooperative Systems},
  pages =	{1--5},
  series =	{Dagstuhl Seminar Proceedings (DagSemProc)},
  ISSN =	{1862-4405},
  year =	{2010},
  volume =	{9371},
  editor =	{S\'{a}ndor Fekete and Stefan Fischer and Martin Riedmiller and Suri Subhash},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/opus/volltexte/2010/2426},
  URN =		{urn:nbn:de:0030-drops-24265},
  doi =		{10.4230/DagSemProc.09371.2},
  annote =	{Keywords: Multi-agent reinforcement learning, decentralized control, job-shop scheduling}
}

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

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