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Control Systems and Reinforcement Learning 🔍
Sean P. Meyn Cambridge University Press (Virtual Publishing), Cambridge, United Kingdom, 2022
英语 [en] · PDF · 14.6MB · 2022 · 📘 非小说类图书 · 🚀/lgli/lgrs/nexusstc/upload/zlib · Save
描述
"A high school student can create deep Q-learning code to control her robot, without any understanding of the meaning of "deep" or "Q", or why the code sometimes fails. This book is designed to explain the science behind reinforcement learning and optimal control in a way that is accessible to students with a background in calculus and matrix algebra. A unique focus is algorithm design to obtain the fastest possible speed of convergence for learning algorithms, along with insight into why reinforcement learning sometimes fails. Advanced stochastic process theory is avoided at the start by substituting random exploration with more intuitive deterministic probing for learning. Once these ideas are understood, it is not difficult to master techniques rooted in stochastic control. These topics are covered in the second part of the book, starting with Markov chain theory and ending with a fresh look at actor-critic methods for reinforcement learning"-- Provided by publisher
备用文件名
nexusstc/Control Systems and Reinforcement Learning/a6c6f4e991fbbbe1bdd4a0f3897408bb.pdf
备用文件名
lgli/Control Systems and Reinforcement Learning.pdf
备用文件名
lgrsnf/Control Systems and Reinforcement Learning.pdf
备用文件名
zlib/Computers/Hardware/Sean Meyn/Control Systems and Reinforcement Learning_21842872.pdf
备选作者
Meyn, Sean
备用出版商
RCOG Press
备用版本
United Kingdom and Ireland, United Kingdom
备用版本
Cambridge ; New York NY, 2022
备用版本
New, PS, 2022
备用版本
1, 20220609
元数据中的注释
{"isbns":["1009051873","1316511960","9781009051873","9781316511961"],"last_page":454,"publisher":"Cambridge University Press"}
备用描述
The book is written for newcomers to reinforcement learning who wish to write code for various applications, from robotics to power systems to supply chains. It also contains advanced material designed to prepare graduate students and professionals for both research and application of reinforcement learning and optimal control techniques
开源日期
2022-06-28
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