Logo

Reinforcement Learning: An Introduction

Large book cover: Reinforcement Learning: An Introduction

Reinforcement Learning: An Introduction
by

Publisher: The MIT Press
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.

Home page url

Download or read it online for free here:
Download link
(12MB, PDF)

Similar books

Book cover: Boosting: Foundations and AlgorithmsBoosting: Foundations and Algorithms
by - The MIT Press
Boosting is an approach to machine learning based on the idea of creating a highly accurate predictor by combining many weak and inaccurate 'rules of thumb'. A remarkably rich theory has evolved around boosting, with connections to a range of topics.
(6490 views)
Book cover: Machine Learning for Data StreamsMachine Learning for Data Streams
by - The MIT Press
This book presents algorithms and techniques used in data stream mining and real-time analytics. Taking a hands-on approach, the book demonstrates the techniques using MOA, allowing readers to try out the techniques after reading the explanations.
(6437 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.
(4815 views)
Book cover: Machine LearningMachine Learning
by - 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.
(16227 views)