MGHRL: Meta Goal-Generation for Hierarchical Reinforcement Learning
Most meta reinforcement learning (meta-RL) methods learn to adapt to new tasks by directly optimizing the parameters of policies over primitive action space. Such algorithms work well in tasks with relatively slight differences. However, when the task dis
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Matthew E. Taylor Yang Yu Edith Elkind Yang Gao (Eds.)
Distributed Artificial Intelligence Second International Conference, DAI 2020 Nanjing, China, October 24–27, 2020 Proceedings
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Lecture Notes in Artificial Intelligence Subseries of Lecture Notes in Computer Science
Series Editors Randy Goebel University of Alberta, Edmonton, Canada Yuzuru Tanaka Hokkaido University, Sapporo, Japan Wolfgang Wahlster DFKI and Saarland University, Saarbrücken, Germany
Founding Editor Jörg Siekmann DFKI and Saarland University, Saarbrücken, Germany
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More information about this subseries at http://www.springer.com/series/1244
Matthew E. Taylor Yang Yu Edith Elkind Yang Gao (Eds.) •
•
•
Distributed Artificial Intelligence Second International Conference, DAI 2020 Nanjing, China, October 24–27, 2020 Proceedings
123
Editors Matthew E. Taylor University of Alberta Edmonton, AB, Canada
Yang Yu Nanjing University Nanjing, China
Edith Elkind University of Oxford Oxford, UK
Yang Gao Nanjing University Nanjing, China
ISSN 0302-9743 ISSN 1611-3349 (electronic) Lecture Notes in Artificial Intelligence ISBN 978-3-030-64095-8 ISBN 978-3-030-64096-5 (eBook) https://doi.org/10.1007/978-3-030-64096-5 LNCS Sublibrary: SL7 – Artificial Intelligence © Springer Nature Switzerland AG 2020 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. 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. The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Switzerland AG The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland
Preface
Lately, there has been tremendous growth in the field of artificial intelligence (AI) in general and in multi-agent systems research in particular. Problems arise where decisions are no longer made by a center but by autonomous and distributed agents. Such decision problems have been recognized as a central research agenda in AI and a fundamental problem in multi-agent
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