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Giuseppe Ciaburro - Hands-On Reinforcement Learning with R

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Implement key reinforcement learning algorithms and techniques using different R packages such as the Markov chain, MDP toolbox, contextual, and OpenAI Gym

Key Features

  • Explore the design principles of reinforcement learning and deep reinforcement learning models
    • Use dynamic programming to solve design issues related to building a self-learning system
    • Learn how to systematically implement reinforcement learning algorithms

      Book Description

      Reinforcement learning (RL) is an integral part of machine learning (ML), and is used to train algorithms. With this book, youll learn how to implement reinforcement learning with R, exploring practical examples such as using tabular Q-learning to control robots.

      Youll begin by learning the basic RL concepts, covering the agent-environment interface, Markov Decision Processes (MDPs), and policy gradient methods. Youll then use Rs libraries to develop a model based on Markov chains. You will also learn how to solve a multi-armed bandit problem using various R packages. By applying dynamic programming and Monte Carlo methods, you will also find the best policy to make predictions. As you progress, youll use Temporal Difference (TD) learning for vehicle routing problem applications. Gradually, youll apply the concepts youve learned to real-world problems, including fraud detection in finance, and TD learning for planning activities in the healthcare sector. Youll explore deep reinforcement learning using Keras, which uses the power of neural networks to increase RLs potential. Finally, youll discover the scope of RL and explore the challenges in building and deploying machine learning models.

      By the end of this book, youll be well-versed with RL and have the skills you need to efficiently implement it with R.

      What you will learn

    • Understand how to use MDP to manage complex scenarios
    • Solve classic reinforcement learning problems such as the multi-armed bandit model
    • Use dynamic programming for optimal policy searching
    • Adopt Monte Carlo methods for prediction
    • Apply TD learning to search for the best path
    • Use tabular Q-learning to control robots
    • Handle environments using the OpenAI library to simulate real-world applications
    • Develop deep Q-learning algorithms to improve model performance

      Who this book is for

      This book is for anyone who wants to learn about reinforcement learning with R from scratch. A solid understanding of R and basic knowledge of machine learning are necessary to grasp the topics covered in the book.

    • Giuseppe Ciaburro: author's other books


    Who wrote Hands-On Reinforcement Learning with R? Find out the surname, the name of the author of the book and a list of all author's works by series.


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    Hands-On Reinforcement Learning with R Get up to speed with building - photo 1
    Hands-On Reinforcement Learning with R
    Get up to speed with building self-learning systems
    using R 3.x
    Giuseppe Ciaburro

    BIRMINGHAM - MUMBAI Hands-On Reinforcement Learning with R Copyright 2019 - photo 2

    BIRMINGHAM - MUMBAI
    Hands-On Reinforcement Learning with R

    Copyright 2019 Packt Publishing

    All rights reserved. No part of this book may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, without the prior written permission of the publisher, except in the case of brief quotations embedded in critical articles or reviews.

    Every effort has been made in the preparation of this book to ensure the accuracy of the information presented. However, the information contained in this book is sold without warranty, either express or implied. Neither the author, nor Packt Publishing or its dealers and distributors, will be held liable for any damages caused or alleged to have been caused directly or indirectly by this book.

    Packt Publishing has endeavored to provide trademark information about all of the companies and products mentioned in this book by the appropriate use of capitals. However, Packt Publishing cannot guarantee the accuracy of this information.

    Commissioning Editor: Sunith Shetty
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    First published: December 2019

    Production reference: 1161219

    Published by Packt Publishing Ltd.
    Livery Place
    35 Livery Street
    Birmingham
    B3 2PB, UK.

    ISBN 978-1-78961-671-2

    www.packt.com

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    Contributors
    About the author

    Giuseppe Ciaburro holds a PhD in environmental technical physics, along with two master's degrees. His research was focused on machine learning applications in the study of urban sound environments. He works at the Built Environment Control Laboratory at the Universit degli Studi della Campania Luigi Vanvitelli, Italy. He has over 18 years' professional experience in programming (Python, R, and MATLAB), first in the field of combustion, and then in acoustics and noise control. He has several publications to his credit.

    About the reviewers

    Antonio L. Amadeu is a data science consultant, passionate about artificial intelligence, and neural networks. He researches and applies machine learning and deep learning algorithms in his daily challenges, solving all types of issues in any business industries. He has worked for big companies like Unilever, Lloyds Bank, TE Connectivity, Microsoft, Samsung, and others. As an aspiring Astrophysicist, he does some research on astronomy object classification using machine and deep learning techniques using Virtual Observatory global environment (IVOA), as well as, participate in researches for Astronomy and Geophysics institute at Universidade de So Paulo.

    Jithin S L has a B.Tech in IT and is currently working as a senior solution architect in the Data Analytics and Artificial Intelligence group at Wipro Pvt Ltd, Dubai. He has been associated with various organizations, such as IBM, Thomson Reuters, Formcept Inc, Flytxt, and Infinite Computer Solutions, and has played key roles in the analytics space. Jithin has reviewed other books, such as Machine Learning with R Cookbook, and R Data Analysis Cookbook. He has submitted many research papers on technology and business at national and international conferences.

    Be energetic, be positive, and be happy: the three Bes make your life easy and successful
    Jithin S L

    I surrender myself to God almighty who helped me to review this book in an effective way.
    I dedicate my work on this book to my dad, Mr. Subbian Asari; my mom, Mrs. Lekshmi; and my sweet sister, Jishma, for their continuous encouragement and support. Special thanks to my bosses, Mathew Joseph and Satheesh Kumar Balan, for their support and for providing continuous opportunities to showcase my skills and capabilities. Last but not least, I would like to thank all my friends.

    Saibal Dutta has been working as an analytical consultant in SAS Research and Development. He is also pursuing a PhD in data mining and machine learning from IIT, Kharagpur. He holds an M.Tech in electronics and communication from the National Institute of Technology, Rourkela. He has worked at TATA communications, Pune, and HCL Technologies Limited, Noida, as a consultant. In his 7 years of consulting experience, he has been associated with global players including IKEA (in Sweden) and Pearson (in the US). His passion for entrepreneurship led him to create his own start-up in the field of data analytics. His areas of expertise include data mining, artificial intelligence, machine learning, image processing, and business consultation.

    Packt is searching for authors like you

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    Preface

    Reinforcement learning is an exciting part of machine learning. It has a variety of uses in technology, ranging from autonomous cars to game playing. Reinforcement learning creates algorithms that can learn and adapt to environmental changes.

    This book provides a hands-on approach to the implementation of reinforcement learning. It will explore interesting practical examples, such as using tabular Q-learning to control robots. It also explores associated methodologies that will have you up and running in no time.

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