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Titlebook: Game-Theoretic Learning and Distributed Optimization in Memoryless Multi-Agent Systems; Tatiana Tatarenko Book 2017 Springer International

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發(fā)表于 2025-3-21 18:03:25 | 只看該作者 |倒序瀏覽 |閱讀模式
書目名稱Game-Theoretic Learning and Distributed Optimization in Memoryless Multi-Agent Systems
編輯Tatiana Tatarenko
視頻videohttp://file.papertrans.cn/381/380525/380525.mp4
概述Presents new, efficient methods for optimization in large-scale multi-agent systems.Develops efficient optimization algorithms for three different information settings in multi-agent systems.Sets opti
圖書封面Titlebook: Game-Theoretic Learning and Distributed Optimization in Memoryless Multi-Agent Systems;  Tatiana Tatarenko Book 2017 Springer International
描述.This book presents new efficient methods for optimization in realistic large-scale, multi-agent systems. These methods do not require the agents to have the full information about the system, but instead allow them to make their local decisions based only on the local information, possibly obtained during communication with their local neighbors. The book, primarily aimed at researchers in optimization and control, considers three different information settings in multi-agent systems: oracle-based, communication-based, and payoff-based. For each of these information types, an efficient optimization algorithm is developed, which leads the system to an optimal state. The optimization problems are set without such restrictive assumptions as convexity of the objective functions, complicated communication topologies, closed-form expressions for costs and utilities, and finiteness of the system’s state space.?.
出版日期Book 2017
關(guān)鍵詞distributed optimization; game-theoretic approach to optimization; learning algorithms; consensus-based
版次1
doihttps://doi.org/10.1007/978-3-319-65479-9
isbn_softcover978-3-319-88039-6
isbn_ebook978-3-319-65479-9
copyrightSpringer International Publishing AG 2017
The information of publication is updating

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沙發(fā)
發(fā)表于 2025-3-21 22:35:33 | 只看該作者
板凳
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n problems are set without such restrictive assumptions as convexity of the objective functions, complicated communication topologies, closed-form expressions for costs and utilities, and finiteness of the system’s state space.?.978-3-319-88039-6978-3-319-65479-9
地板
發(fā)表于 2025-3-22 05:56:22 | 只看該作者
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發(fā)表于 2025-3-22 18:16:56 | 只看該作者
https://doi.org/10.1007/978-3-642-92345-6Due to the emergence of distributed networked systems, problems of cooperative control in multi-agent systems have gained a lot of attention over the recent years. Some examples of networked multi-agent systems are smart grids, social networks, autonomous vehicle teams, processors in machine learning scenarios, etc.
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發(fā)表于 2025-3-22 23:50:46 | 只看該作者
https://doi.org/10.1007/978-3-322-96216-4In this section, a background of the game theory with applications to optimization in multi-agent systems is presented.
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發(fā)表于 2025-3-23 02:45:51 | 只看該作者
Willi Paul Adams,Erich AngermannThis chapter deals with multi-agent systems whose objective is modeled by means of potential games and which are endowed with . As it was discussed in the previous chapter,
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Amerikastudien / American StudiesThis chapter studies the way to apply stochastic approximation procedure, known as the Robbins–Monro procedure [RM51], to . as well as to . and ..
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