| 书目名称 | Game-Theoretic Learning and Distributed Optimization in Memoryless Multi-Agent Systems | | 编辑 | Tatiana Tatarenko | | 视频video | | | 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 | | 图书封面 |  | | 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 | | 关键词 | distributed optimization; game-theoretic approach to optimization; learning algorithms; consensus-based | | 版次 | 1 | | doi | https://doi.org/10.1007/978-3-319-65479-9 | | isbn_softcover | 978-3-319-88039-6 | | isbn_ebook | 978-3-319-65479-9 | | copyright | Springer International Publishing AG 2017 |
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发表于 2025-3-21 18:03:25
