**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 Course in Machine Learning**

by

**Hal DaumÃ© III**-

**ciml.info**

Tis is a set of introductory materials that covers most major aspects of modern machine learning (supervised and unsupervised learning, large margin methods, probabilistic modeling, etc.). It's focus is on broad applications with a rigorous backbone.

(

**13010**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.

(

**11285**views)

**Algorithms for Reinforcement Learning**

by

**Csaba Szepesvari**-

**Morgan and Claypool Publishers**

We focus on those algorithms of reinforcement learning that build on the theory of dynamic programming. We give a comprehensive catalog of learning problems, describe the core ideas, followed by the discussion of their properties and limitations.

(

**3881**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.

(

**21675**views)