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Cooperative Multi-Agent Systems from the Reinforcement Learning Perspective – Challenges, Algorithms, and an Application Gabel, Thomas Multi-agent reinforcement learning decentralized control job-shop scheduling 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. Schloss Dagstuhl – Leibniz-Zentrum für Informatik Thomas Gabel 2010 Is Part Of Dagstuhl Seminar Proceedings, Volume 9371, Algorithmic Methods for Distributed Cooperative Systems (2010) InProceedings Text doc-type:ResearchArticle publishedVersion application/pdf doi:10.4230/DagSemProc.09371.2 urn:nbn:de:0030-drops-24265 https://drops.dagstuhl.de/entities/document/10.4230/DagSemProc.09371.2 eng https://creativecommons.org/licenses/by/4.0/legalcode