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

Tatarenko, Tatiana

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. 

Details

Published by: Springer

Publication Date: 2018-08-15

Format: Paperback

ISBN-13: 9783319880396

DOI: 10.1007/978-3-319-65479-9

Dimensions: 235cm x155cm

Pages: 171