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Lapan - Deep Reinforcement Learning Hands-On

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Lapan - Deep Reinforcement Learning Hands-On
Deep Reinforcement Learning Hands-On
Lapan
Deep Reinforcement Learning Hands-On
Lapan - Deep Reinforcement Learning Hands-On: Apply modern RL methods, with deep Q-networks, value iteration, policy gradients, TRPO, AlphaGo Zero and more
Deep Reinforcement Learning Hands-On: Apply modern RL methods, with deep Q-networks, value iteration, policy gradients, TRPO, AlphaGo Zero and more
Lapan
Deep Reinforcement Learning Hands-On: Apply modern RL methods, with deep Q-networks, value iteration, policy gradients, TRPO, AlphaGo Zero and more

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Deep Reinforcement Learning Hands-On
Table of Contents
Deep Reinforcement Learning Hands-On
Deep Reinforcement Learning Hands-On

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.

Acquisition Editors: Frank Pohlmann, Suresh Jain

Project Editor: Kishor Rit

Technical Editor: Nidhisha Shetty

Proofreader: Tom Jacob

Indexer: Tejal Daruwale Soni

Graphics: Sandip Tadge

Production Coordinator: Shantanu Zagade

First published: June 2018

Production reference: 1150618

Published by Packt Publishing Ltd.

Livery Place

35 Livery Street

Birmingham B3 2PB, UK.

ISBN 978-1-78883-424-7

www.packtpub.com

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Contributors
About the author
Maxim Lapan is a deep learning enthusiast and independent researcher His - photo 2

Maxim Lapan is a deep learning enthusiast and independent researcher. His background and 15 years' work expertise as a software developer and a systems architect lays from low-level Linux kernel driver development to performance optimization and design of distributed applications working on thousands of servers. With vast work experiences in big data, Machine Learning, and large parallel distributed HPC and nonHPC systems, he has a talent to explain a gist of complicated things in simple words and vivid examples. His current areas of interest lie in practical applications of Deep Learning, such as Deep Natural Language Processing and Deep Reinforcement Learning.

Maxim lives in Moscow, Russian Federation, with his family, and he works for an Israeli start-up as a Senior NLP developer.

I'd like to thank my family: wife Olga and kids Ksenia, Julia, and Fedor, for patience and support. It was a challenging time, writing this book and it wouldn't be possible without you, thanks! Julia and Fedor did a great job gathering samples for MiniWoB (Chapter 13, Web Navigation ) and testing ConnectFour agent's playing skills (Chapter 18, AlphaGo Zero ).

I also want to thank the technical reviewers, Oleg Vasilev and Mikhail Yurushkin, for their valuable comments and suggestions about the book contents.

About the reviewers

Basem O. F. Alijla received his Ph.D. degree in intelligent systems from USM, Malaysia, in 2015. He is currently an assistant professor with Software Development Department, IUG in Palestine. He has authored number of technical papers published in journals and international conferences. His current research interest include, Optimization, Machine Learning, and Data mining.

Oleg Vasilev is a professional with a background in Computer Science and Data Engineering. His university program is Applied Mathematics and Informatics in NRU HSE, Moscow, with a major in Distributed Systems. He is a staff member on a Git-course, Practical_RL and Practical_DL, taught on-campus in HSE and YSDA. Oleg's previous work experience includes working in Dialog Systems Group, Yandex, as Data Scientist. He currently holds a position of Vice President of Infrastructure Management in GoTo Lab, an educational corporation, and he works for Digital Contact as a software engineer.

I'd like to thank Alexander Panin (@justheuristic), my mentor, for opening the world of Machine Learning to me. I am deeply grateful to other Russian researchers who helped me in mastering Computer Science: Pavel Shvechikov , Alexander Grishin , Valery Kharitonov , Alexander Fritzler , Pavel Ostyakov , Michail Konobeev , Dmitrii Vetrov , and Alena Ilyna . I also want to thank my friends and family for their kind support.

Mikhail Yurushkin holds a PhD in Applied Mathematics. His areas of research are high performance computing and optimizing compilers development. He was involved in the development of a state-of-the-art optimizing parallelizing compiler system. Mikhail is a senior lecturer at SFEDU university, Rostov on Don, Russia. He teaches advanced DL courses, namely Computer Vision and NLP. Mikhail has worked for over 7 years in cross-platform native C++ development, machine learning, and deep learning. Now he works as an individual consultant in ML/DL fields.

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Preface

The topic of this book is Reinforcement Learningwhich is a subfield of Machine Learningfocusing on the general and challenging problem of learning optimal behavior in complex environment. The learning process is driven only by reward value and observations obtained from the environment. This model is very general and can be applied to many practical situations from playing games to optimizing complex manufacture processes.

Due to flexibility and generality, the field of Reinforcement Learning is developing very quickly and attracts lots of attention both from researchers trying to improve existing or create new methods, as well as from practitioners interested in solving their problems in the most efficient way.

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