Learning in Cooperative Multi-Agent Systems

Language: English

Published by Südwestdeutscher Verlag Für Hochschulschriften Aug 2009, 2009

3838110366 / 9783838110363

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This item is printed on demand - Print on Demand Titel. Neuware -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.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 192 pp. Deutsch.…

Seller Inventory # 9783838110363

Title
Learning in Cooperative Multi-Agent Systems
Author
Thomas Gabel
Publisher
Südwestdeutscher Verlag Für Hochschulschriften Aug 2009
Publication year
2009
Condition
Neu
Binding
Taschenbuch
Language
English
ISBN 10
3838110366
ISBN 13
9783838110363
Item weight
304 grams
Dimensions
220x150x13 mm

buchversandmimpf2000

Emtmannsberg, BAYE, Germany

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