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

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

автор Thomas G.T. издательство Книга по Требованию Локальные и корпоративные сети
Cтраниц 192        мягкая обложка       
Аннотация: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. Many real-world applications are well-suited to be formulated in terms of spatially or functionally distributed entities. He identifies a subclass of general decentralized decision-making problems that features provably reduced complexity. 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. Job-shop scheduling represents one such application. In a distributed system, a number of individually acting agents coexist. All algorithms proposed are evaluated in the scope of various established scheduling benchmark problems. In order to achieve a common goal, coordinated cooperation between the agents is crucial.
Автор: Thomas G.T.
Издательство: Книга по Требованию
Год:2009
Цена:
3620 руб
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Поиск: Learning in Cooperative Multi-Agent Systems. Distributed Reinforcement Learning Algorithms and their Application to Scheduling Problems
НОВЫЙ ГОД СИМВОЛ 2013 ГОДАУКРАШЕНИЯ
КАРНАВАЛ НА НОВЫЙ ГОД РАСПРОДАЖА
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