Learning in Cooperative Multi-Agent Systems : Distributed Reinforcement Learning Algorithms and their Application to Scheduling Problems
Language: English
Published by Südwestdeutscher Verlag Für Hochschulschriften, 2009
- Softcover
- New
Seller: AHA-BUCH GmbH, Einbeck, GermanyAHA-BUCH GmbH
AbeBooks seller since August 14, 2006
Condition: New
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Add to basketItem description from seller
nach der Bestellung gedruckt Neuware - Printed after ordering - 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.…
Seller Inventory # 9783838110363
- Title
- Learning in Cooperative Multi-Agent Systems : Distributed Reinforcement Learning Algorithms and their Application to Scheduling Problems
- Author
- Thomas Gabel
- Publisher
- Südwestdeutscher Verlag Für Hochschulschriften
- Publication year
- 2009
- Condition
- Neu
- Binding
- Taschenbuch
- Language
- English
- ISBN 10
- 3838110366
- ISBN 13
- 9783838110363
- Item weight
- 304 grams
- Dimensions
- 220x150x13 mm
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