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

  • Book
  • © 2017

Overview

Authors:
  1. Tatiana Tatarenko

    1. TU Darmstadt, Darmstadt, Germany

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  • 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 optimization problems without common restrictive assumptions

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Table of contents (5 chapters)

  1. Front Matter

    Pages i-ix
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  2. Introduction

    • Tatiana Tatarenko
    Pages 1-5

  3. Game Theory and Multi-Agent Optimization

    • Tatiana Tatarenko
    Pages 7-26

  4. Logit Dynamics in Potential Games with Memoryless Players

    • Tatiana Tatarenko
    Pages 27-91

  5. Stochastic Methods in Distributed Optimization and Game-Theoretic Learning

    • Tatiana Tatarenko
    Pages 93-155

  6. Conclusion

    • Tatiana Tatarenko
    Pages 157-158

  7. Back Matter

    Pages 159-171
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Keywords

About this book

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. 



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 Postolică, zbMath 1415.91002, 2019)

Authors and Affiliations

  • TU Darmstadt, Darmstadt, Germany

    Tatiana Tatarenko

About the author

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. 



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