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Ravichandiran - Hands-On Reinforcement Learning with Python: Master Reinforcement and Deep Reinforcement Learning Using OpenAI Gym and TensorFlow

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Hands-On Reinforcement Learning with Python: Master Reinforcement and Deep Reinforcement Learning Using OpenAI Gym and TensorFlow: summary, description and annotation

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Reinforcement learning is a self-evolving type of machine learning that takes us closer to achieving true artificial intelligence. This easy-to-follow guide explains everything from scratch using rich examples written in Python.;Cover; Title Page; Copyright and Credits; Dedication; Packt Upsell; Contributors; Table of Contents; Preface; Chapter 1: Introduction to Reinforcement Learning; What is RL?; RL algorithm; How RL differs from other ML paradigms; Elements of RL; Agent; Policy function; Value function; Model; Agent environment interface; Types of RL environment; Deterministic environment; Stochastic environment; Fully observable environment; Partially observable environment; Discrete environment; Continuous environment; Episodic and non-episodic environment; Single and multi-agent environment; RL platforms.

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Hands-On Reinforcement Learning with Python Master reinforcement and deep - photo 1
Hands-On Reinforcement Learning with Python
Master reinforcement and deep reinforcement learning using OpenAI Gym and TensorFlow
Sudharsan Ravichandiran

BIRMINGHAM - MUMBAI Hands-On Reinforcement Learningwith Python Copyright 2018 - photo 2

BIRMINGHAM - MUMBAI
Hands-On Reinforcement Learningwith Python

Copyright 2018 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
Acquisition Editor: Namrata Patil
Content Development Editor: Amrita Noronha
Technical Editor: Jovita Alva
Copy Editor: Safis Editing
Project Coordinator: Shweta H Birwatkar
Proofreader: Safis Editing
Indexer: Rekha Nair
Graphics: Jisha Chirayil
Production Coordinator: Shantanu Zagade

First published: June 2018

Production reference: 1260618

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

ISBN 978-1-78883-652-4

www.packtpub.com

To my adorable parents to my brother Karthikeyan and to my bestest friend - photo 3

To my adorable parents, to my brother, Karthikeyan, and to my bestest friend, Nikhil Aditya.
Sudharsan Ravichandiran
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Contributors
About the author

Sudharsan Ravichandiran is a data scientist, researcher, artificial intelligence enthusiast, and YouTuber (search for Sudharsan reinforcement learning). He completed his bachelors in information technology at Anna University. His area of research focuses on practical implementations of deep learning and reinforcement learning, which includes natural language processing and computer vision. He used to be a freelance web developer and designer and has designed award-winning websites. He is an open source contributor and loves answering questions on Stack Overflow.

I would like to thank my amazing parents and my brother, Karthikeyan, for constantly inspiring and motivating me throughout this journey. My big thanks and gratitude to my bestest friend, Nikhil Aditya, who is literally the bestest, and to my editor, Amrita, and to my Soeor. Without all their support, it would have been impossible to complete this book.
About the reviewers

Sujit Pal is a Technology Research Director at Elsevier Labs, an advanced technology group within the Reed-Elsevier Group of companies. His areas of interests include semantic search, natural language processing, machine learning, and deep learning. At Elsevier, he has worked on several initiatives involving search quality measurement and improvement, image classification and duplicate detection, and annotation and ontology development for medical and scientific corpora. He has co-authored a book on deep learning with Antonio Gulli and writes about technology on his blog, Salmon Run.

Suriyadeepan Ramamoorthy is an AI researcher and engineer from Puducherry, India. His primary areas of research are natural language understanding and reasoning. He actively blogs about deep learning.

At SAAMA technologies, he applies advanced deep learning techniques for biomedical text analysis. He is a free software evangelist who is actively involved in community development activities at FSFTN. His other interests include community networks, data visualization and creative coding.

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Preface

Reinforcement learning is a self-evolving type of machine learning that takes us closer to achieving true artificial intelligence. This easy-to-follow guide explains everything from scratch using rich examples written in Python.

Who this book is for

This book is intended for machine learning developers and deep learning enthusiasts who are interested in artificial intelligence and want to learn about reinforcement learning from scratch. Read this book and become a reinforcement learning expert by implementing practical examples at work or in projects. Having some knowledge of linear algebra, calculus, and the Python programming language will help you understand the flow of the book.

What this book covers

, Introduction to Reinforcement Learning, helps us understand what reinforcement learning is and how it works. We will learn about various elements of reinforcement learning, such as agents, environments, policies, and models, and we will see different types of environments, platforms, and libraries used for reinforcement learning. Later in the chapter, we will see some of the applications of reinforcement learning.

, Getting Started with OpenAI and TensorFlow, helps us set up our machine for various reinforcement learning tasks. We will learn how to set up our machine by installing Anaconda, Docker, OpenAI Gym, Universe, and TensorFlow. Then we will learn how to simulate agents in OpenAI Gym, and we will see how to build a video game bot. We will also learn the fundamentals of TensorFlow and see how to use TensorBoard for visualizations.

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