Sample Efficient Multiagent Learning in the Presence of Markovian Agents
The problem of Multiagent Learning (or MAL) is concerned with the study of how intelligent entities can learn and adapt in the presence of other such entities that are simultaneously adapting. The problem is often studied in the stylized settings provided
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Doran Chakraborty
Sample Efficient Multiagent Learning in the Presence of Markovian Agents
Studies in Computational Intelligence Volume 523
Series Editor Janusz Kacprzyk, Warsaw, Poland
For further volumes: http://www.springer.com/series/7092
Doran Chakraborty
Sample Efficient Multiagent Learning in the Presence of Markovian Agents
ABC
Doran Chakraborty Department of Computer Science The University of Texas Austin, Texas USA
ISSN 1860-949X ISBN 978-3-319-02605-3 DOI 10.1007/978-3-319-02606-0
ISSN 1860-9503 (electronic) ISBN 978-3-319-02606-0 (eBook)
Springer Cham Heidelberg New York Dordrecht London Library of Congress Control Number: 2013949609 c Springer International Publishing Switzerland 2014 This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. Exempted from this legal reservation are brief excerpts in connection with reviews or scholarly analysis or material supplied specifically for the purpose of being entered and executed on a computer system, for exclusive use by the purchaser of the work. Duplication of this publication or parts thereof is permitted only under the provisions of the Copyright Law of the Publisher’s location, in its current version, and permission for use must always be obtained from Springer. Permissions for use may be obtained through RightsLink at the Copyright Clearance Center. Violations are liable to prosecution under the respective Copyright Law. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. While the advice and information in this book are believed to be true and accurate at the date of publication, neither the authors nor the editors nor the publisher can accept any legal responsibility for any errors or omissions that may be made. The publisher makes no warranty, express or implied, with respect to the material contained herein.
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I dedicate this work to my parents, Late. Salil Kumar Chakraborty, and Banani Chakraborty.
Abstract
The problem of multiagent learning (or MAL) is concerned with the study of how agents can learn and adapt in the presence of other agents that are simultaneously adapting. The problem is often studied in the stylized settings provided by repeated matrix games. The goal of this book is to develop MAL algorithms for such a setting that achieve a new set of objectives which have not been previously achieved.
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