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Maxim Lapan - Deep Reinforcement Learning Hands-On: Apply modern RL methods to practical problems of chatbots, robotics, discrete optimization, web automation, and more, 2nd Edition

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Maxim Lapan Deep Reinforcement Learning Hands-On: Apply modern RL methods to practical problems of chatbots, robotics, discrete optimization, web automation, and more, 2nd Edition
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Deep Reinforcement Learning Hands-On: Apply modern RL methods to practical problems of chatbots, robotics, discrete optimization, web automation, and more, 2nd Edition: summary, description and annotation

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New edition of the bestselling guide to deep reinforcement learning and how its used to solve complex real-world problems. Revised and expanded to include multi-agent methods, discrete optimization, RL in robotics, advanced exploration techniques, and more

Key Features
  • Second edition of the bestselling introduction to deep reinforcement learning, expanded with six new chapters
  • Learn advanced exploration techniques including noisy networks, pseudo-count, and network distillation methods
  • Apply RL methods to cheap hardware robotics platforms
Book Description

Deep Reinforcement Learning Hands-On, Second Edition is an updated and expanded version of the bestselling guide to the very latest reinforcement learning (RL) tools and techniques. It provides you with an introduction to the fundamentals of RL, along with the hands-on ability to code intelligent learning agents to perform a range of practical tasks.

With six new chapters devoted to a variety of up-to-the-minute developments in RL, including discrete optimization (solving the Rubiks Cube), multi-agent methods, Microsofts TextWorld environment, advanced exploration techniques, and more, you will come away from this book with a deep understanding of the latest innovations in this emerging field.

In addition, you will gain actionable insights into such topic areas as deep Q-networks, policy gradient methods, continuous control problems, and highly scalable, non-gradient methods. You will also discover how to build a real hardware robot trained with RL for less than $100 and solve the Pong environment in just 30 minutes of training using step-by-step code optimization.

In short, Deep Reinforcement Learning Hands-On, Second Edition, is your companion to navigating the exciting complexities of RL as it helps you attain experience and knowledge through real-world examples.

What you will learn
  • Understand the deep learning context of RL and implement complex deep learning models
  • Evaluate RL methods including cross-entropy, DQN, actor-critic, TRPO, PPO, DDPG, D4PG, and others
  • Build a practical hardware robot trained with RL methods for less than $100
  • Discover Microsofts TextWorld environment, which is an interactive fiction games platform
  • Use discrete optimization in RL to solve a Rubiks Cube
  • Teach your agent to play Connect 4 using AlphaGo Zero
  • Explore the very latest deep RL research on topics including AI chatbots
  • Discover advanced exploration techniques, including noisy networks and network distillation techniques
Who this book is for

Some fluency in Python is assumed. Sound understanding of the fundamentals of deep learning will be helpful. This book is an introduction to deep RL and requires no background in RL

Table of Contents
  1. What Is Reinforcement Learning?
  2. OpenAI Gym
  3. Deep Learning with PyTorch
  4. The Cross-Entropy Method
  5. Tabular Learning and the Bellman Equation
  6. Deep Q-Networks
  7. Higher-Level RL libraries
  8. DQN Extensions
  9. Ways to Speed up RL
  10. Stocks Trading Using RL
  11. Policy Gradients an Alternative
  12. The Actor-Critic Method
  13. Asynchronous Advantage Actor-Critic
  14. Training Chatbots with RL
  15. The TextWorld environment
  16. Web Navigation
  17. Continuous Action Space
  18. RL in Robotics
  19. Trust Regions PPO, TRPO, ACKTR, and SAC
  20. Black-Box Optimization in RL
  21. Advanced exploration
  22. Beyond Model-Free Imagination
  23. AlphaGo Zero
  24. RL in Discrete Optimisation
  25. Multi-agent RL

Maxim Lapan: author's other books


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Maxim Lapan - Deep Reinforcement Learning Hands-On: Apply modern RL methods to practical problems of chatbots, robotics, discrete optimization, web automation, and more, 2nd Edition
Deep Reinforcement Learning Hands-On: Apply modern RL methods to practical problems of chatbots, robotics, discrete optimization, web automation, and more, 2nd Edition
Maxim Lapan
Deep Reinforcement Learning Hands-On: Apply modern RL methods to practical problems of chatbots, robotics, discrete optimization, web automation, and more, 2nd Edition

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Deep Reinforcement Learning Hands-On Second Edition Apply modern RL methods - photo 1

Deep Reinforcement Learning Hands-On

Second Edition

Apply modern RL methods to practical problems of chatbots, robotics, discrete optimization, web automation, and more

Maxim Lapan

BIRMINGHAM - MUMBAI Deep Reinforcement Learning Hands-On Second Edition - photo 2

BIRMINGHAM - MUMBAI

Deep Reinforcement Learning Hands-On

Second Edition

Copyright 2020 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 authors, 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.

Producer: Jonathan Malysiak

Acquisition Editor Peer Reviews: Suresh Jain

Content Development Editors: Joanne Lovell and Chris Nelson

Technical Editor: Saby Dsilva

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Proofreader: Safis Editing

Indexer: Rekha Nair

Presentation Designer: Sandip Tadge

First published: June 2018

Second edition: January 2020

Production reference: 1300120

Published by Packt Publishing Ltd.

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ISBN 978-1-83882-699-4

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Contributors
About the authors

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 covers everything from low-level Linux kernel driver development to performance optimization and the design of distributed applications working on thousands of servers. With extensive work experience in big data, machine learning, and large parallel distributed HPC and non-HPC systems, he has the ability to explain complicated things using simple words and vivid examples. His current areas of interest surround the practical applications of deep learning, such as deep natural language processing and deep reinforcement learning.

Maxim lives in Moscow, Russia, with his family.

Id like to thank my family: my wife, Olga, and my children, Ksenia, Julia, and Fedor, for their patience and support. It was a challenging time writing this book and it wouldn't have been possible without you, so thanks! Julia and Fedor did a great job of gathering samples for MiniWoB (Chapter 16, Web Navigation) and testing the Connect 4 agent's playing skills (Chapter 23, AlphaGo Zero).

About the reviewers

Mikhail Yurushkin holds a PhD. His areas of research are high-performance computing and optimizing compiler development. Mikhail is a senior lecturer at SFEDU university, Rostov-on-Don, Russia. He teaches advanced deep learning courses on computer vision and NLP. Mikhail has worked for over eight years in cross-platform native C++ development, machine learning, and deep learning. He is an entrepreneur and founder of several technological start-ups, including BroutonLab Data Science Company, which specializes in the development of AI-powered software products.

Per-Arne Andersen is a PhD student in deep reinforcement learning at the University of Agder, Norway. He has authored several technical papers on reinforcement learning for games and received the best student award from the British Computer Society for his research into model-based reinforcement learning. Per-Arne is also an expert on network security, having worked in the field since 2012. His current research interests include machine learning, deep learning, network security, and reinforcement learning.

Sergey Kolesnikov is an industrial and academic research engineer with over five years' experience in machine learning, deep learning, and reinforcement learning. He's currently working on industrial applications that deal with CV, NLP, and RecSys, and is involved in reinforcement learning academic research. He is also interested in sequential decision making and psychology. Sergey is a NeurIPS competition winner and an open source evangelist. He is also the creator of Catalyst a high-level PyTorch ecosystem for accelerated deep learning/reinforcement learning research and development.

Preface

The topic of this book is reinforcement learning (RL), which is a subfield of machine learning (ML); it focuses on the general and challenging problem of learning optimal behavior in a complex environment. The learning process is driven only by the 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 manufacturing processes.

Due to its flexibility and generality, the field of RL is developing very quickly and attracting lots of attention, both from researchers who are trying to improve existing methods or create new methods and from practitioners interested in solving their problems in the most efficient way.

Why I wrote this book

This book was written as an attempt to fill the obvious gap in practical and structured information about RL methods and approaches. On the one hand, there is lots of research activity all around the world. New research papers are being published almost every day, and a large portion of deep learning (DL) conferences, such as Neural Information Processing Systems (NeurIPS) or the International Conference on Learning Representations (ICLR), are dedicated to RL methods. There are also several large research groups focusing on the application of RL methods to robotics, medicine, multi-agent systems, and others.

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