**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.

(

**20997**views)

**Elements of Causal Inference: Foundations and Learning Algorithms**

by

**J. Peters, D. Janzing, B. Schölkopf**-

**The MIT Press**

This book offers a self-contained and concise introduction to causal models and how to learn them from data. The book teaches readers how to use causal models: how to compute intervention distributions, how to infer causal models from data ...

(

**5882**views)

**A First Encounter with Machine Learning**

by

**Max Welling**-

**University of California Irvine**

The book you see before you is meant for those starting out in the field of machine learning, who need a simple, intuitive explanation of some of the most useful algorithms that our field has to offer. A prelude to the more advanced text books.

(

**11797**views)

**Introduction to Machine Learning for the Sciences**

by

**Titus Neupert, et al.**-

**arXiv.org**

This is an introductory machine learning course specifically developed with STEM students in mind, written by the theoretical Condensed Matter Theory group at the University of Zurich. We discuss supervised, unsupervised, and reinforcement learning.

(

**2815**views)