Reinforcement Learning Algorithms: Analysis and Applications
Synopsis
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.Book details
- Edition:
- 1st ed. 2021
- Series:
- Studies in Computational Intelligence (Book 883)
- Author:
- Boris Belousov, Hany Abdulsamad, Pascal Klink, Simone Parisi, Jan Peters
- ISBN:
- 9783030411886
- Related ISBNs:
- 9783030411879
- Publisher:
- Springer International Publishing
- Pages:
- N/A
- Reading age:
- Not specified
- Includes images:
- Yes
- Date of addition:
- 2021-03-02
- Usage restrictions:
- Copyright
- Copyright date:
- 2021
- Copyright by:
- The Editor
- Adult content:
- No
- Language:
- English
- Categories:
- Computers and Internet, Nonfiction, Technology