**Reinforcement Learning and Optimal Control**

by Dimitri P. Bertsekas

**Publisher**: Athena Scientific 2019**Number of pages**: 276

**Description**:

The purpose of the book is to consider large and challenging multistage decision problems, which can be solved in principle by dynamic programming and optimal control, but their exact solution is computationally intractable. We discuss solution methods that rely on approximations to produce suboptimal policies with adequate performance.

Download or read it online for free here:

**Download link**

(multiple PDF files)

## Similar books

**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.

(

**12914**views)

**Gaussian Processes for Machine Learning**

by

**Carl E. Rasmussen, Christopher K. I. Williams**-

**The MIT Press**

Gaussian processes provide a principled, practical, probabilistic approach to learning in kernel machines. The treatment is comprehensive and self-contained, targeted at researchers and students in machine learning and applied statistics.

(

**28348**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 ...

(

**6539**views)

**Learning Deep Architectures for AI**

by

**Yoshua Bengio**-

**Now Publishers**

This book discusses the principles of learning algorithms for deep architectures, in particular those exploiting as building blocks unsupervised learning of single-layer models such as Restricted Boltzmann Machines, used to construct deeper models.

(

**8319**views)