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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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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: Apply modern RL methods, with deep Q-networks, value iteration, policy gradients, TRPO, AlphaGo Zero and more: summary, description and annotation

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This practical guide will teach you how deep learning (DL) can be used to solve complex real-world problems. About This Book Explore deep reinforcement learning (RL), from the first principles to the latest algorithms Evaluate high-profile RL methods, including value iteration, deep Q-networks, policy gradients, TRPO, PPO, DDPG, D4PG, evolution strategies and genetic algorithms Keep up with the very latest industry developments, including AI-driven chatbots Who This Book Is For Some fluency in Python is assumed. Basic deep learning (DL) approaches should be familiar to readers and some practical experience in DL will be helpful. This book is an introduction to deep reinforcement learning (RL) and requires no background in RL. What You Will Learn Understand the DL context of RL and implement complex DL models Learn the foundation of RL: Markov decision processes Evaluate RL methods including Cross-entropy, DQN, Actor-Critic, TRPO, PPO, DDPG, D4PG and others Discover how to deal with discrete and continuous action spaces in various environments Defeat Atari arcade games using the value iteration method Create your own OpenAI Gym environment to train a stock trading agent Teach your agent to play Connect4 using AlphaGo Zero Explore the very latest deep RL research on topics including AI-driven chatbots In Detail Recent developments in reinforcement learning (RL), combined with deep learning (DL), have seen unprecedented progress made towards training agents to solve complex problems in a human-like way. Googles use of algorithms to play and defeat the well-known Atari arcade games has propelled the field to prominence, and researchers are generating new ideas at a rapid pace. Deep Reinforcement Learning Hands-On is a comprehensive guide to the very latest DL tools and their limitations. You will evaluate methods including Cross-entropy and policy gradients, before applying them to real-world environments. Take on both the Atari set of virtual games and family favorites such as Connect4. The book provides an introduction to the basics of RL, giving you the know-how to code intelligent learning agents to take on a formidable array of practical tasks. Discover how to implement Q-learning on grid world environments, teach your agent to buy and trade stocks, and find out how natural language models are driving the boom in chatbots. Style and approach Deep Reinforcement Learning Hands-On explains the art of building self-learning agents using algori ...

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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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