Reinforcement Learning: An Introduction
by Richard S. Sutton, Andrew G. Barto
Publisher: The MIT Press 2017
ISBN/ASIN: 0262193981
ISBN-13: 9780262193986
Number of pages: 445
Description:
Reinforcement learning is a computational approach to learning whereby an agent tries to maximize the total amount of reward it receives when interacting with a complex, uncertain environment. In this book, Richard Sutton and Andrew Barto provide a clear and simple account of the key ideas and algorithms of reinforcement learning. Their discussion ranges from the history of the field's intellectual foundations to the most recent developments and applications. The only necessary mathematical background is familiarity with elementary concepts of probability.
Download or read it online for free here:
Download link
(12MB, PDF)
Similar books
Boosting: Foundations and Algorithmsby Robert E. Schapire, Yoav Freund - The MIT Press
Boosting is an approach to machine learning based on the idea of creating a highly accurate predictor by combining many weak and inaccurate 'rules of thumb'. A remarkably rich theory has evolved around boosting, with connections to a range of topics.
(8374 views)
Machine Learning and Data Mining: Lecture Notesby Aaron Hertzmann - University of Toronto
Contents: Introduction to Machine Learning; Linear Regression; Nonlinear Regression; Quadratics; Basic Probability Theory; Probability Density Functions; Estimation; Classification; Gradient Descent; Cross Validation; Bayesian Methods; and more.
(11849 views)
A Survey of Statistical Network Modelsby A. Goldenberg, A.X. Zheng, S.E. Fienberg, E.M. Airoldi - arXiv
We begin with the historical development of statistical network modeling and then we introduce some examples in the network literature. Our subsequent discussion focuses on prominent static and dynamic network models and their interconnections.
(10549 views)
The Hundred-Page Machine Learning Bookby Andriy Burkov
This is the first successful attempt to write an easy to read book on machine learning that isn't afraid of using math. It's also the first attempt to squeeze a wide range of machine learning topics in a systematic way and without loss in quality.
(12351 views)