**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

**Reinforcement Learning**

by

**C. Weber, M. Elshaw, N. M. Mayer**-

**InTech**

This book describes and extends the scope of reinforcement learning. It also shows that there is already wide usage in numerous fields. Reinforcement learning can tackle control tasks that are too complex for traditional controllers.

(

**15801**views)

**Introduction To Machine Learning**

by

**Nils J Nilsson**

This book concentrates on the important ideas in machine learning, to give the reader sufficient preparation to make the extensive literature on machine learning accessible. The author surveys the important topics in machine learning circa 1996.

(

**21660**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.

(

**5454**views)

**Machine Learning**

by

**Abdelhamid Mellouk, Abdennacer Chebira**-

**InTech**

Neural machine learning approaches, Hamiltonian neural networks, similarity discriminant analysis, machine learning methods for spoken dialogue simulation and optimization, linear subspace learning for facial expression analysis, and more.

(

**11283**views)