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Learning in Cooperative Multi-Agent Systems: Distributed Reinforcement Learning Algorithms and their Application to Scheduling Problems - Softcover
Synopsis
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.
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About the Author
Thomas Gabel, Dr. rer. nat., studied Computer Scienceat the University of Kaiserslautern. Subsequently, hewas working as scientific researcher at the University of Osnabrück with focus on learning in multi-agent systems, reinforcement learning, as well as knowledge management and case-based reasoning. He received his doctoral degree in 2009.
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- PublisherSüdwestdeutscher Verlag für Hochschulschriften
- Publication date2009
- ISBN 10 3838110366
- ISBN 13 9783838110363
- BindingPaperback
- LanguageEnglish
- Number of pages192
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Learning in Cooperative Multi-Agent Systems
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Südwestdeutscher Verlag Für Hochschulschriften AG Co. KG Mai 2017, 2017
ISBN 10: 3838110366
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Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - 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. 192 pp. Deutsch. Seller Inventory # 9783838110363
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Learning in Cooperative Multi-Agent Systems : Distributed Reinforcement Learning Algorithms and their Application to Scheduling Problems
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Südwestdeutscher Verlag Für Hochschulschriften AG Co. KG, 2009
ISBN 10: 3838110366
ISBN 13: 9783838110363
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Taschenbuch. Condition: Neu. 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
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Learning in Cooperative Multi-Agent Systems
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Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. 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 functio. Seller Inventory # 5405420
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Learning in Cooperative Multi-Agent Systems
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Südwestdeutscher Verlag Für Hochschulschriften AG Co. KG Mai 2017, 2017
ISBN 10: 3838110366
ISBN 13: 9783838110363
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Taschenbuch. Condition: Neu. 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.Books on Demand GmbH, Überseering 33, 22297 Hamburg 192 pp. Deutsch. Seller Inventory # 9783838110363
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Learning in Cooperative Multi-Agent Systems | Distributed Reinforcement Learning Algorithms and their Application to Scheduling Problems
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Südwestdeutscher Verlag für Hochschulschriften AG Co. KG, 2017
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Taschenbuch. Condition: Neu. 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. Seller Inventory # 101491389
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