Learning in Cooperative Multi-Agent Systems | Distributed Reinforcement Learning Algorithms and their Application to Scheduling Problems

Thomas Gabel

ISBN 10: 3838110366 / ISBN 13: 9783838110363

Verlag: Südwestdeutscher Verlag für Hochschulschriften AG Co. KG, 2017

Beschreibung

Learning in Cooperative Multi-Agent Systems | Distributed Reinforcement Learning Algorithms and their Application to Scheduling Problems | Thomas Gabel | Taschenbuch | 192 S. | Deutsch | 2017 | Südwestdeutscher Verlag für Hochschulschriften AG Co. KG | EAN 9783838110363 | Verantwortliche Person für die EU: Südwestdt. Verl. f. Hochschulschrift., Brivibas Gatve 197, 1039 RIGA, LETTLAND, customerservice[at]vdm-vsg[dot]de | Anbieter: preigu. Bestandsnummer des Verkäufers 101491389

Bibliografische Details

Titel: Learning in Cooperative Multi-Agent Systems ...

Verlag: Südwestdeutscher Verlag für Hochschulschriften AG Co. KG

Erscheinungsdatum: 2017

Einband: Taschenbuch

Zustand: Neu

Über diesen Titel

Inhaltsangabe:

In a distributed system, a number of individually acting agents coexist. In order to achieve a common goal, coordinated cooperation between the agents is crucial. Many real-world applications are well-suited to be formulated in terms of spatially or functionally distributed entities. Job-shop scheduling represents one such application. Multi-agent reinforcement learning (RL) methods allow for automatically acquiring cooperative policies based solely on a specification of the desired joint behavior of the whole system. However, the decentralization of the control and observation of the system among independent agents has a significant impact on problem complexity. The author Thomas Gabel addresses the intricacy of learning and acting in multi-agent systems by two complementary approaches. He identifies a subclass of general decentralized decision-making problems that features provably reduced complexity. Moreover, he presents various novel model-free multi-agent RL algorithms that are capable of quickly obtaining approximate solutions in the vicinity of the optimum. All algorithms proposed are evaluated in the scope of various established scheduling benchmark problems.