Reinforcement Learning Algorithms: Analysis and Applications (Hardcover, 1st ed. 2021)

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 Universitat 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.

General

Imprint:	Springer Nature Switzerland AG
Country of origin:	Switzerland
Series:	Studies in Computational Intelligence, 883
Release date:	2021
First published:	2021
Editors:	Boris Belousov • Hany Abdulsamad • Pascal Klink • Simone Parisi • Jan Peters
Dimensions:	235 x 155mm (L x W)
Format:	Hardcover
Pages:	206
Edition:	1st ed. 2021
ISBN-13:	978-3-03-041187-9
Categories:	Books > Computing & IT > General theory of computing > Data structures Books > Computing & IT > Computer programming > Algorithms & procedures Books > Computing & IT > Applications of computing > Artificial intelligence > General
LSN:	3-03-041187-7
Barcode:	9783030411879

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