Game-Theoretic Learning and Distributed Optimization in Memoryless Multi-Agent Systems

By: Tatiana Tatarenko

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Hardcover | 28 September 2017

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Hardcover
184 Pages

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23.5 x 15.88 x 1.91

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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.

Industry Reviews

"This book offers new efficient methods for optimization and control in multi-agent systems through the agency of game-theoretic learning. ... The book represents an important scientific contribution in the field of optimization for the multi-agent systems." (Vasile PostolicÄ, zbMath 1415.91002, 2019)

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