Intrinsically Motivated Learning in Natural and Artificial Systems
It has become clear to researchers in robotics and adaptive behaviour that current approaches are yielding systems with limited autonomy and capacity for self-improvement. To learn autonomously and in a cumulative fashion is one of the hallmarks of intell
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Gianluca Baldassarre • Marco Mirolli Editors
Intrinsically Motivated Learning in Natural and Artificial Systems
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Editors Gianluca Baldassarre Marco Mirolli Istituto di Scienze e Tecnologie della Cognizione Consiglio Nazionale delle Ricerche Rome, Italy
ISBN 978-3-642-32374-4 ISBN 978-3-642-32375-1 (eBook) DOI 10.1007/978-3-642-32375-1 Springer Heidelberg New York Dordrecht London Library of Congress Control Number: 2013930554 ACM Computing Classification (1998): I.2, J.4, J.5 © Springer-Verlag Berlin Heidelberg 2013 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. Printed on acid-free paper Springer is part of Springer Science+Business Media (www.springer.com)
Contents
Intrinsically Motivated Learning Systems: An Overview . . . . . . . . . . . . . . . . . . . Gianluca Baldassarre and Marco Mirolli Part I
1
General Overviews on Intrinsic Motivations
Intrinsic Motivation and Reinforcement Learning .. . . . . .. . . . . . . . . . . . . . . . . . . . Andrew G. Barto
17
Functions and Mechanisms of Intrinsic Motivations.. . . .. . . . . . . . . . . . . . . . . . . . Marco Mirolli and Gianluca Baldassarre
49
Exploration from Generalization Mediated by Multiple Controllers . . . . . . Peter Dayan
73
Part II
Prediction-Based Intrinsic Motivation Mechanisms
Maximizing Fun by Creating Data with Easily Reducible Subjective Complexity .. . . . . . . . .
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