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

Gabel, Thomas

doi:10.4230/DagSemProc.09371.2

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

Author Thomas Gabel

Part of: Volume: Dagstuhl Seminar Proceedings, Volume 9371
Part of: Series: Dagstuhl Seminar Proceedings (DagSemProc)
License: Creative Commons Attribution 4.0 International license
Publication Date: 2010-04-22

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DagSemProc.09371.2.pdf

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Document Identifiers

DOI: 10.4230/DagSemProc.09371.2
URN: urn:nbn:de:0030-drops-24265

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Keywords

Multi-agent reinforcement learning
decentralized control
job-shop scheduling

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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.

Cite As Get BibTex

Thomas Gabel. Cooperative Multi-Agent Systems from the Reinforcement Learning Perspective – Challenges, Algorithms, and an Application. In Algorithmic Methods for Distributed Cooperative Systems. Dagstuhl Seminar Proceedings, Volume 9371, pp. 1-5, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2010) https://doi.org/10.4230/DagSemProc.09371.2

Author Details

Thomas Gabel

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