Binding: Hardcover
Description: 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.
Title: Control Systems And Reinforcement Learning
Author(s): Meyn Sean (University Of Florida)
Publisher: Cambridge University Press
Barcode: 9781316511961
Pages: 450 Pages, Worked Examples Or Exercises
Publication Date: 7/28/2022
Category: Machine Learning