Distributed policy search reinforcement learning for job-shop scheduling tasks

We interpret job-shop scheduling problems as sequential decision problems that are handled by independent learning agents. These agents act completely decoupled from one another and employ probabilistic dispatching policies for which we propose a compact representation using a small set of real-valu...

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Veröffentlicht in:	International journal of production research 2012-01, Vol.50 (1), p.41-61
Hauptverfasser:	Gabel, Thomas, Riedmiller, Martin
Format:	Artikel
Sprache:	eng
Schlagworte:	Decision making models distributed control distributed reinforcement learning Job shops job-shop scheduling Learning mulit-agent systems policy search Scheduling Sequential scheduling Studies
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