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

Tatarenko, Tatiana

ISBN 10: 3319654780 / ISBN 13: 9783319654782

Verlag: Springer, 2017

In. Bestandsnummer des Verkäufers ria9783319654782_new

Inhaltsangabe:

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. 

Über die Autorin bzw. den Autor: Tatiana Tatarenko received her Ph.D. from the Control Methods and Robotics Lab at the Technical University of Darmstadt, Germany in 2017. In 2011, she graduated with honors in Mathematics, focusing on statistics and stochastic processes, from Lomonosov Moscow State University, Russia. Her main research interests are in the fields of distributed optimization, game-theoretic learning, and stochastic processes in networked multi-agent systems. Currently, Dr. Tatarenko is a research assistant at TU Darmstadt, where she teaches and supervises students. 

Bibliografische Details

Titel: Game-Theoretic Learning and Distributed ...
Verlag: Springer
Erscheinungsdatum: 2017
Einband: Hardcover
Zustand: New