Graesser Laura - Foundations of Deep Reinforcement Learning: Theory and Practice in Python
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- Book:Foundations of Deep Reinforcement Learning: Theory and Practice in Python
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- Year:2020;2019
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The Contemporary Introduction to Deep Reinforcement Learning that Combines Theory and Practice
Deep reinforcement learning (deep RL) combines deep learning and reinforcement learning, in which artificial agents learn to solve sequential decision-making problems. In the past decade deep RL has achieved remarkable results on a range of problems, from single and multiplayer gamessuch as Go, Atari games, and DotA 2to robotics.
Foundations of Deep Reinforcement Learning is an introduction to deep RL that uniquely combines both theory and implementation. It starts with intuition, then carefully explains the theory of deep RL algorithms, discusses implementations in its companion software library SLM Lab, and finishes with the practical details of getting deep RL to work.
This guide is ideal for both computer science students and software engineers who are familiar with basic machine learning concepts and have a working understanding of Python.
- Understand each key aspect of a deep RL problem
- Explore policy- and value-based algorithms, including REINFORCE, SARSA, DQN, Double DQN, and Prioritized Experience Replay (PER)
- Delve into combined algorithms, including Actor-Critic and Proximal Policy Optimization (PPO)
- Understand how algorithms can be parallelized synchronously and asynchronously
- Run algorithms in SLM Lab and learn the practical implementation details for getting deep RL to work
- Explore algorithm benchmark results with tuned hyperparameters
- Understand how deep RL environments are designed
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This book provides an accessible introduction to deep reinforcement learning covering the mathematical concepts behind popular algorithms as well as their practical implementation. I think the book will be a valuable resource for anyone looking to apply deep reinforcement learning in practice.
Volodymyr Mnih, lead developer of DQN
An excellent book to quickly develop expertise in the theory, language, and practical implementation of deep reinforcement learning algorithms. A limpid exposition which uses familiar notation; all the most recent techniques explained with concise, readable code, and not a page wasted in irrelevant detours: it is the perfect way to develop a solid foundation on the topic.
Vincent Vanhoucke, principal scientist, Google
As someone who spends their days trying to make deep reinforcement learning methods more useful for the general public, I can say that Laura and Kengs book is a welcome addition to the literature. It provides both a readable introduction to the fundamental concepts in reinforcement learning as well as intuitive explanations and code for many of the major algorithms in the field. I imagine this will become an invaluable resource for individuals interested in learning about deep reinforcement learning for years to come.
Arthur Juliani, senior machine learning engineer, Unity Technologies
Until now, the only way to get to grips with deep reinforcement learning was to slowly accumulate knowledge from dozens of different sources. Finally, we have a book bringing everything together in one place.
Matthew Rahtz, ML researcher, ETH Zrich
Theory and Practice in Python
Laura Graesser
Wah Loon Keng
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The authors and publisher have taken care in the preparation of this book, but make no expressed or implied warranty of any kind and assume no responsibility for errors or omissions. No liability is assumed for incidental or consequential damages in connection with or arising out of the use of the information or programs contained herein.
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Library of Congress Control Number: 2019948417
Copyright 2020 Pearson Education, Inc.
Cover illustration by Wacomka/Shutterstock
SLM Lab is an MIT-licensed open source project.
All rights reserved. This publication is protected by copyright, and permission must be obtained from the publisher prior to any prohibited reproduction, storage in a retrieval system, or transmission in any form or by any means, electronic, mechanical, photocopying, recording, or likewise. For information regarding permissions, request forms and the appropriate contacts within the Pearson Education Global Rights & Permissions Department, please visit www.pearson.com/permissions.
ISBN-13: 978-0-13-517238-4
ISBN-10: 0-13-517238-1
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For those people who make me feel that anything is possible
Laura
For my wife Daniela
Keng
In April of 2019, OpenAIs Five bots played in a Dota 2 competition match against 2018 human world champions, OG. Dota 2 is a complex, multiplayer battle arena game where players can choose different characters. Winning a game requires strategy, teamwork, and quick decisions. Building an artificial intelligence to compete in this game, with so many variables and a seemingly infinite search space for optimization, seems like an insurmountable challenge. Yet OpenAIs bots won handily and, soon after, went on to win over 99% of their matches against public players. The innovation underlying this achievement was deep reinforcement learning.
Although this development is recent, reinforcement learning and deep learning have both been around for decades. However, a significant amount of new research combined with the increasing power of GPUs have pushed the state of the art forward. This book gives the reader an introduction to deep reinforcement learning and distills the work done over the last six years into a cohesive whole.
While training a computer to beat a video game may not be the most practical thing to do, its only a starting point. Reinforcement learning is an area of machine learning that is useful for solving sequential decision-making problemsthat is, problems that are solved over time. This applies to almost any endeavorbe it playing a video game, walking down the street, or driving a car.
Laura Graesser and Wah Loon Keng have put together an approachable introduction to a complicated topic that is at the forefront of what is new in machine learning. Not only have they brought to bear their research into many papers on the topic; they created an open source library, SLM Lab, to help others get up and running quickly with deep reinforcement learning. SLM Lab is written in Python on top of PyTorch, but readers only need familiarity with Python. Readers intending to use TensorFlow or some other library as their deep learning framework of choice will still get value from this book as it introduces the concepts and problem formulations for deep reinforcement learning solutions.
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