DagSemProc.09371.2.pdf
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Cooperative Multi-Agent Systems from the Reinforcement Learning Perspective – Challenges, Algorithms, and an Application
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Dagstuhl Seminar Proceedings, Volume 9371
Series: Dagstuhl Seminar Proceedings (DagSemProc) - License:
Creative Commons Attribution 4.0 International license
- Publication Date: 2010-04-22
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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
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.
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- Multi-agent reinforcement learning
- decentralized control
- job-shop scheduling
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