**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

**An Introduction to Probabilistic Programming**

by

**Jan-Willem van de Meent, et al.**-

**arXiv.org**

This text is designed to be a graduate-level introduction to probabilistic programming. It provides a thorough background for anyone wishing to use a probabilistic programming system, and introduces the techniques needed to build these systems.

(

**802**views)

**The LION Way: Machine Learning plus Intelligent Optimization**

by

**Roberto Battiti, Mauro Brunato**-

**Lionsolver, Inc.**

Learning and Intelligent Optimization (LION) is the combination of learning from data and optimization applied to solve complex problems. This book is about increasing the automation level and connecting data directly to decisions and actions.

(

**18599**views)

**Information Theory, Inference, and Learning Algorithms**

by

**David J. C. MacKay**-

**Cambridge University Press**

A textbook on information theory, Bayesian inference and learning algorithms, useful for undergraduates and postgraduates students, and as a reference for researchers. Essential reading for students of electrical engineering and computer science.

(

**19391**views)

**Machine Learning and Data Mining: Lecture Notes**

by

**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.

(

**4795**views)