Reinforcement Learning Algorithms: Analysis and Applications /
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...
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| Other Authors: | , , , , |
| Format: | eBook |
| Language: | English |
| Published: |
Cham :
Springer International Publishing : Imprint: Springer,
2021.
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| Edition: | 1st ed. 2021. |
| Series: | Studies in Computational Intelligence,
883 |
| Subjects: | |
| Online Access: | Connect to the full text of this electronic book |
| Summary: | 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. |
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| Physical Description: | 1 online resource (VIII, 206 pages 45 illustrations, 35 illustrations in color.) |
| ISBN: | 9783030411886 |
| ISSN: | 1860-9503 ; |
| DOI: | 10.1007/978-3-030-41188-6 |