Model-Based Reinforcement Learning
from data to continuous actions with a python-based toolbox
$307.85
- Hardcover
272 pages
- Release Date
5 December 2022
Summary
Model-Based Reinforcement Learning: Bridging Theory and Practice
Explore a comprehensive and practical approach to reinforcement learning
Reinforcement learning is an essential paradigm of machine learning, wherein an intelligent agent performs actions that ensure optimal behavior from devices. While this paradigm of machine learning has gained tremendous success and popularity in recent years, previous scholarship has focused either on theory—optimal contro…
Book Details
| ISBN-13: | 9781119808572 |
|---|---|
| ISBN-10: | 111980857X |
| Series: | IEEE Press Series on Control Systems Theory and Applications |
| Author: | Milad Farsi, Jun Liu, Maria Domenica Di Benedetto |
| Publisher: | John Wiley & Sons Inc |
| Imprint: | Wiley-IEEE Press |
| Format: | Hardcover |
| Number of Pages: | 272 |
| Release Date: | 5 December 2022 |
| Weight: | 631g |
| Dimensions: | 152mm x 229mm x 16mm |
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About The Author
Milad Farsi
Milad Farsi received the B.S. degree in Electrical Engineering (Electronics) from the University of Tabriz in 2010. He obtained his M.S. degree also in Electrical Engineering (Control Systems) from the Sahand University of Technology in 2013. Moreover, he gained industrial experience as a Control System Engineer between 2012 and 2016. Later, he acquired the Ph.D. degree in Applied Mathematics from the University of Waterloo, Canada, in 2022, and he is currently a Postdoctoral Fellow at the same institution. His research interests include control systems, reinforcement learning, and their applications in robotics and power electronics.
Jun Liu received the Ph.D. degree in Applied Mathematics from the University of Waterloo, Canada, in 2010. He is currently an Associate Professor of Applied Mathematics and a Canada Research Chair in Hybrid Systems and Control at the University of Waterloo, Canada, where he directs the Hybrid Systems Laboratory. From 2012 to 2015, he was a Lecturer in Control and Systems Engineering at the University of Sheffield. During 2011 and 2012, he was a Postdoctoral Scholar in Control and Dynamical Systems at the California Institute of Technology. His main research interests are in the theory and applications of hybrid systems and control, including rigorous computational methods for control design with applications in cyber-physical systems and robotics.
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