Manisha Biswas - Reinforcement Learning : With Open AI, TensorFlow and Keras Using Python

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Abhishek Nandy and Manisha Biswas

Reinforcement Learning With Open AI, TensorFlow and Keras Using Python

Abhishek Nandy

Rm HIG L-2/4, Bldg Swaranika Co-Opt HSG, Kolkata, West Bengal, India

Manisha Biswas

North 24 Parganas, West Bengal, India

Any source code or other supplementary material referenced by the author in this book is available to readers on GitHub via the books product page, located at www.apress.com/978-1-4842-3110-4 . For more detailed information, please visit http://www.apress.com/source-code .

ISBN 978-1-4842-3284-2 e-ISBN 978-1-4842-3285-9

https://doi.org/10.1007/978-1-4842-3285-9

Library of Congress Control Number: 2017962867

Abhishek Nandy and Manisha Biswas 2018

This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed.

Trademarked names, logos, and images may appear in this book. Rather than use a trademark symbol with every occurrence of a trademarked name, logo, or image we use the names, logos, and images only in an editorial fashion and to the benefit of the trademark owner, with no intention of infringement of the trademark. The use in this publication of trade names, trademarks, service marks, and similar terms, even if they are not identified as such, is not to be taken as an expression of opinion as to whether or not they are subject to proprietary rights.

While the advice and information in this book are believed to be true and accurate at the date of publication, neither the authors nor the editors nor the publisher can accept any legal responsibility for any errors or omissions that may be made. The publisher makes no warranty, express or implied, with respect to the material contained herein.

Printed on acid-free paper

Distributed to the book trade worldwide by Springer Science+Business Media New York, 233 Spring Street, 6th Floor, New York, NY 10013. Phone 1-800-SPRINGER, fax (201) 348-4505, e-mail orders-ny@springer-sbm.com, or visit www.springeronline.com. Apress Media, LLC is a California LLC and the sole member (owner) is Springer Science + Business Media Finance Inc (SSBM Finance Inc). SSBM Finance Inc is a Delaware corporation.

Introduction

This book is primarily based on a Machine Learning subset known as Reinforcement Learning. We cover the basics of Reinforcement Learning with the help of the Python programming language and touch on several aspects, such as Q learning, MDP, RL with Keras, and OpenAI Gym and OpenAI Environment, and also cover algorithms related to RL.

Users need a basic understanding of programming in Python to benefit from this book.

The book is meant for people who want to get into Machine Learning and learn more about Reinforcement Learning.

Acknowledgments

I want to dedicate this book to my parents.

Abhishek Nandy

I want to dedicate this book to my mom and dad. Thank you to my teachers and my co-author, Abhishek Nandy. Thanks also to Abhishek Sur, who mentors me at work and helps me adapt to new technologies. I would also like to dedicate this book to my company, InSync Tech-Fin Solutions Ltd., where I started my career and have grown professionally.

Manisha Biswas

Contents

Index

About the Authors and About the Technical Reviewer

About the Authors

Abhishek Nandy

has a B.Tech. in information technology and considers himself a constant learner. He is a Microsoft MVP in the Windows platform, an Intel Black Belt Developer, as well as an Intel software innovator. Abhishek has a keen interest in artificial intelligence, IoT, and game development. He is currently serving as an application architect at an IT firm and consults in AI and IoT, as well does projects in AI, Machine Learning, and deep learning. He is also an AI trainer and drives the technical part of Intel AI student developer program. He was involved in the first Make in India initiative, where he was among the top 50 innovators and was trained in IIMA.

Manisha Biswas

has a B.Tech. in information technology and currently works as a software developer at InSync Tech-Fin Solutions Ltd in Kolkata, India. She is involved in several areas of technology, including web development, IoT, soft computing, and artificial intelligence. She is an Intel Software innovator and was awarded the Shri Dewang Mehta IT Awards 2016 by NASSCOM, a certificate of excellence for top academic scores. She very recently formed a Women in Technology community in Kolkata, India to empower women to learn and explore new technologies. She likes to invent things, create something new, and invent a new look for the old things. When not in front of her terminal, she is an explorer, a foodie, a doodler, and a dreamer. She is always very passionate to share her knowledge and ideas with others. She is following her passion currently by sharing her experiences with the community so that others can learn, which lead her to become Google Women Techmakers, Kolkata Chapter Lead.

About the Technical Reviewer

Avirup Basu

is an IoT application developer at Prescriber360 Solutions. He is a researcher in robotics and has published papers through the IEEE.

Abhishek Nandy and Manisha Biswas 2018

Abhishek Nandy and Manisha Biswas Reinforcement Learning

1. Reinforcement Learning Basics

Abhishek Nandy 1 and Manisha Biswas 2

(1)

Rm HIG L-2/4, Bldg Swaranika Co-Opt HSG, Kolkata, West Bengal, India

(2)

North 24 Parganas, West Bengal, India

This chapter is a brief introduction to Reinforcement Learning (RL) and includes some key concepts associated with it.

In this chapter, we talk about Reinforcement Learning as a core concept and then define it further. We show a complete flow of how Reinforcement Learning works. We discuss exactly where Reinforcement Learning fits into artificial intelligence (AI). After that we define key terms related to Reinforcement Learning. We start with agents and then touch on environments and then finally talk about the connection between agents and environments.

What Is Reinforcement Learning?

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