Logo

Algorithms for Reinforcement Learning

Large book cover: Algorithms for Reinforcement Learning

Algorithms for Reinforcement Learning
by

Publisher: Morgan and Claypool Publishers
ISBN/ASIN: 1608454924
Number of pages: 98

Description:
In this book, we focus on those algorithms of reinforcement learning that build on the powerful theory of dynamic programming. We give a fairly comprehensive catalog of learning problems, describe the core ideas, note a large number of state of the art algorithms, followed by the discussion of their theoretical properties and limitations.

Home page url

Download or read it online for free here:
Download link
(1.6MB, PDF)

Similar books

Book cover: Introduction To Machine LearningIntroduction To Machine Learning
by
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.
(29388 views)
Book cover: A Brief Introduction to Machine Learning for EngineersA Brief Introduction to Machine Learning for Engineers
by - arXiv.org
This monograph provides the starting point to the literature that every engineer new to machine learning needs. It offers a basic and compact reference that describes key ideas and principles in simple terms and within a unified treatment.
(6668 views)
Book cover: Bayesian Reasoning and Machine LearningBayesian Reasoning and Machine Learning
by - Cambridge University Press
The book is designed for final-year undergraduate students with limited background in linear algebra and calculus. Comprehensive and coherent, it develops everything from basics to advanced techniques within the framework of graphical models.
(22418 views)
Book cover: An Introduction to Probabilistic ProgrammingAn Introduction to Probabilistic Programming
by - 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.
(4807 views)