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Studies in Computational Intelligence
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Reinforcement Learning Algorithms: Analysis and Applications

Editors: Belousov, B., Abdulsamad, H., Klink, P., Parisi, S., Peters, J. (Eds.)

  • Provides recent research on reinforcement learning algorithms
  • Presents the analysis and application alike
  • Written by respected experts in the field 
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  • ISBN 978-3-030-41188-6
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  • Due: December 8, 2020
  • ISBN 978-3-030-41187-9
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  • Immediate ebook access, if available*, with your print order
About this book

This book reviews research developments in diverse areas of reinforcement learning such as model-free actor-critic methods, model-based learning and control, information geometry of policy searches, reward design, and exploration in biology and the behavioral sciences. Special emphasis is placed on advanced ideas, algorithms, methods, and applications.

The contributed papers gathered here grew out of a lecture course on reinforcement learning held by Prof. Jan Peters in the winter semester 2018/2019 at Technische Universität Darmstadt.

The book is intended for reinforcement learning students and researchers with a firm grasp of linear algebra, statistics, and optimization. Nevertheless, all key concepts are introduced in each chapter, making the content self-contained and accessible to a broader audience.

About the authors

Boris Belousov is a Ph.D. student at Technische Universität Darmstadt, Germany, advised by Prof. Jan Peters. He received his M.Sc. degree from the University of Erlangen-Nuremberg, Germany, in 2016, supported by a DAAD scholarship for academic excellence. Boris is now working toward combining optimal control and information theory with applications to robotics and reinforcement learning.

Hany Abdulsamad is a Ph.D. student at the TU Darmstadt, Germany. He graduated with a Master’s degree in Automation and Control from the faculty of Electrical Engineering and Information Technology at the TU Darmstadt. His research interests range from optimal control and trajectory optimization to reinforcement learning and robotics. Hany’s current research focuses on learning hierarchical structures for system identification and control.

After graduating with a Master’s degree in Autonomous Systems from the Technische Universität Darmstadt, Pascal Klink pursued his Ph.D. studies at the Intelligent Autonomous Systems Group of the TU Darmstadt, where he developed methods for reinforcement learning in unstructured, partially observable real-world environments. Currently, he is investigating curriculum learning methods and how to use them to facilitate learning in these environments.  

Simone Parisi joined Prof. Jan Peter’s Intelligent Autonomous System lab in October 2014 as a Ph.D. student. Before pursuing his Ph.D., Simone completed his M.Sc. in Computer Science Engineering at the Politecnico di Milano, Italy, and at the University of Queensland, Australia, under the supervision of Prof. Marcello Restelli and Dr. Matteo Pirotta. Simone is currently working to develop reinforcement learning algorithms that can achieve autonomous learning in real-world tasks with little to no human intervention. His research interests include, among others, reinforcement learning, robotics, dimensionality reduction, exploration, intrinsic motivation, and multi-objective optimization. He has collaborated with Prof. Emtiyaz Khan and Dr. Voot Tangkaratt of RIKEN AIP in Tokyo, and his work has been presented at universities and research institutes in the US, Germany, Japan, and Holland.

Jan Peters is a Full Professor of Intelligent Autonomous Systems at the Computer Science Department of the Technische Universität Darmstadt and an adjunct senior research scientist at the Max-Planck Institute for Intelligent Systems, where he heads the Robot Learning Group (combining the Empirical Inference and Autonomous Motion departments). Jan Peters has received numerous awards, most notably the Dick Volz Best US PhD Thesis Runner Up Award, the Robotics: Science & Systems - Early Career Spotlight Award, the IEEE Robotics & Automation Society’s Early Career Award, and the International Neural Networks Society’s Young Investigator Award.


Buy this book

eBook $109.00
price for USA in USD
  • The eBook version of this title will be available soon
  • Due: January 5, 2021
  • ISBN 978-3-030-41188-6
  • Digitally watermarked, DRM-free
  • Included format:
  • ebooks can be used on all reading devices
Hardcover $149.99
price for USA in USD
  • Customers within the U.S. and Canada please contact Customer Service at +1-800-777-4643, Latin America please contact us at +1-212-460-1500 (24 hours a day, 7 days a week). Pre-ordered printed titles are excluded from promotions.
  • Due: December 8, 2020
  • ISBN 978-3-030-41187-9
  • Free shipping for individuals worldwide. Please be advised Covid-19 shipping restrictions apply. Please review prior to ordering.
  • Immediate ebook access, if available*, with your print order
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Bibliographic Information

Bibliographic Information
Book Title
Reinforcement Learning Algorithms: Analysis and Applications
Editors
  • Boris Belousov
  • Hany Abdulsamad
  • Pascal Klink
  • Simone Parisi
  • Jan Peters
Series Title
Studies in Computational Intelligence
Series Volume
883
Copyright
2021
Publisher
Springer International Publishing
Copyright Holder
The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG
eBook ISBN
978-3-030-41188-6
DOI
10.1007/978-3-030-41188-6
Hardcover ISBN
978-3-030-41187-9
Series ISSN
1860-949X
Edition Number
1
Number of Pages
VII, 189
Number of Illustrations
9 b/w illustrations, 35 illustrations in colour
Topics

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