• Complain

Laura Graesser - Foundations of Deep Reinforcement Learning: Theory and Practice in Python (Addison-Wesley Data & Analytics Series)

Here you can read online Laura Graesser - Foundations of Deep Reinforcement Learning: Theory and Practice in Python (Addison-Wesley Data & Analytics Series) full text of the book (entire story) in english for free. Download pdf and epub, get meaning, cover and reviews about this ebook. year: 2019, publisher: Addison-Wesley Professional, genre: Children. Description of the work, (preface) as well as reviews are available. Best literature library LitArk.com created for fans of good reading and offers a wide selection of genres:

Romance novel Science fiction Adventure Detective Science History Home and family Prose Art Politics Computer Non-fiction Religion Business Children Humor

Choose a favorite category and find really read worthwhile books. Enjoy immersion in the world of imagination, feel the emotions of the characters or learn something new for yourself, make an fascinating discovery.

No cover
  • Book:
    Foundations of Deep Reinforcement Learning: Theory and Practice in Python (Addison-Wesley Data & Analytics Series)
  • Author:
  • Publisher:
    Addison-Wesley Professional
  • Genre:
  • Year:
    2019
  • Rating:
    3 / 5
  • Favourites:
    Add to favourites
  • Your mark:
    • 60
    • 1
    • 2
    • 3
    • 4
    • 5

Foundations of Deep Reinforcement Learning: Theory and Practice in Python (Addison-Wesley Data & Analytics Series): summary, description and annotation

We offer to read an annotation, description, summary or preface (depends on what the author of the book "Foundations of Deep Reinforcement Learning: Theory and Practice in Python (Addison-Wesley Data & Analytics Series)" wrote himself). If you haven't found the necessary information about the book — write in the comments, we will try to find it.

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
Register your book for convenient access to downloads, updates, and/or corrections as they become available. See inside book for details.

Laura Graesser: author's other books


Who wrote Foundations of Deep Reinforcement Learning: Theory and Practice in Python (Addison-Wesley Data & Analytics Series)? Find out the surname, the name of the author of the book and a list of all author's works by series.

Foundations of Deep Reinforcement Learning: Theory and Practice in Python (Addison-Wesley Data & Analytics Series) — read online for free the complete book (whole text) full work

Below is the text of the book, divided by pages. System saving the place of the last page read, allows you to conveniently read the book "Foundations of Deep Reinforcement Learning: Theory and Practice in Python (Addison-Wesley Data & Analytics Series)" online for free, without having to search again every time where you left off. Put a bookmark, and you can go to the page where you finished reading at any time.

Light

Font size:

Reset

Interval:

Bookmark:

Make
Contents
About This eBook ePUB is an open industry-standard format for eBooks However - photo 1
About This eBook

ePUB is an open, industry-standard format for eBooks. However, support for ePUB and its many features varies across reading devices and applications. Use your device or app settings to customize the presentation to your liking. Settings that you can customize often include font, font size, single or double column, landscape or portrait mode, and figures that you can click or tap to enlarge. For additional information about the settings and features on your reading device or app, visit the device manufacturers Web site.

Many titles include programming code or configuration examples. To optimize the presentation of these elements, view the e-book in single-column, landscape mode and adjust the font size to the smallest setting. In addition to presenting code and configurations in the reflowable text format, we have included images of the code that mimic the presentation found in the print book; therefore, where the reflowable format may compromise the presentation of the code listing, you will see a Click here to view code image link. Click the link to view the print-fidelity code image. To return to the previous page viewed, click the Back button on your device or app.

Praise for Foundations of Deep Reinforcement Learning

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

Foundations of Deep Reinforcement Learning
Foundations of Deep Reinforcement Learning Theory and Practice in Python Addison-Wesley Data Analytics Series - image 2
Foundations of Deep Reinforcement Learning

Theory and Practice in Python

Laura Graesser
Wah Loon Keng

Foundations of Deep Reinforcement Learning Theory and Practice in Python Addison-Wesley Data Analytics Series - image 3

Boston Columbus New York San Francisco Amsterdam Cape Town
Dubai London Madrid Milan Munich Paris Montreal Toronto Delhi Mexico City
So Paulo Sydney Hong Kong Seoul Singapore Taipei Tokyo

Many of the designations used by manufacturers and sellers to distinguish their products are claimed as trademarks. Where those designations appear in this book, and the publisher was aware of a trademark claim, the designations have been printed with initial capital letters or in all capitals.

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.

For information about buying this title in bulk quantities, or for special sales opportunities (which may include electronic versions; custom cover designs; and content particular to your business, training goals, marketing focus, or branding interests), please contact our corporate sales department at or (800) 382-3419.

For government sales inquiries, please contact .

For questions about sales outside the U.S., please contact .

Visit us on the Web: informit.com/aw

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

3 20

For those people who make me feel that anything is possible
Laura

For my wife Daniela
Keng

Foreword

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.

Next page
Light

Font size:

Reset

Interval:

Bookmark:

Make

Similar books «Foundations of Deep Reinforcement Learning: Theory and Practice in Python (Addison-Wesley Data & Analytics Series)»

Look at similar books to Foundations of Deep Reinforcement Learning: Theory and Practice in Python (Addison-Wesley Data & Analytics Series). We have selected literature similar in name and meaning in the hope of providing readers with more options to find new, interesting, not yet read works.


Reviews about «Foundations of Deep Reinforcement Learning: Theory and Practice in Python (Addison-Wesley Data & Analytics Series)»

Discussion, reviews of the book Foundations of Deep Reinforcement Learning: Theory and Practice in Python (Addison-Wesley Data & Analytics Series) and just readers' own opinions. Leave your comments, write what you think about the work, its meaning or the main characters. Specify what exactly you liked and what you didn't like, and why you think so.