Book Image

Deep Reinforcement Learning Hands-On - Second Edition

By : Maxim Lapan
5 (2)
Book Image

Deep Reinforcement Learning Hands-On - Second Edition

5 (2)
By: Maxim Lapan

Overview of this book

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 Rubik's Cube), multi-agent methods, Microsoft's 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.
Table of Contents (28 chapters)
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To get the most out of this book

All the chapters in this book describing RL methods have the same structure: in the beginning, we discuss the motivation of the method, its theoretical foundation, and the idea behind it. Then, we follow several examples of the method applied to different environments with the full source code.

You can use the book in different ways:

  1. To quickly become familiar with some method, you can read only the introductory part of the relevant chapter
  2. To get a deeper understanding of the way the method is implemented, you can read the code and the comments around it
  3. To gain a deep familiarity with the method (the best way to learn, I believe) you can try to reimplement the method and make it work, using the provided source code as a reference point

In any case, I hope the book will be useful for you!

Download the example code files

You can download the example code files for this book from your account at If you purchased this book elsewhere, you can visit and register to have the files emailed directly to you.

You can download the code files by following these steps:

  1. Log in or register at
  2. Select the Support tab.
  3. Click on Code Downloads.
  4. Enter the name of the book in the Search box and follow the on-screen instructions.

Once the file is downloaded, please make sure that you unzip or extract the folder using the latest version of:

  • WinRAR / 7-Zip for Windows
  • Zipeg / iZip / UnRarX for Mac
  • 7-Zip / PeaZip for Linux

The code bundle for the book is also hosted on GitHub at In case there’s an update to the code, it will be updated on the existing GitHub repository.

We also have other code bundles from our rich catalog of books and videos available at Check them out!

Download the color images

We also provide a PDF file that has color images of the screenshots/diagrams used in this book. You can download it here:

Conventions used

There are a number of text conventions used throughout this book.

CodeInText: Indicates code words in text, database table names, folder names, filenames, file extensions, pathnames, dummy URLs, user input, and Twitter handles. For example; “Mount the downloaded WebStorm-10*.dmg disk image file as another disk in your system.”

A block of code is set as follows:

def grads_func(proc_name, net, device, train_queue):
    envs = [make_env() for _ in range(NUM_ENVS)]
    agent = ptan.agent.PolicyAgent(
        lambda x: net(x)[0], device=device, apply_softmax=True)
    exp_source = ptan.experience.ExperienceSourceFirstLast(
        envs, agent, gamma=GAMMA, steps_count=REWARD_STEPS)
    batch = []
    frame_idx = 0
    writer = SummaryWriter(comment=proc_name)

Any command-line input or output is written as follows:

rl_book_samples/Chapter11$ ./ --cuda -n final

Bold: Indicates a new term, an important word, or words that you see on the screen, for example, in menus or dialog boxes, also appear in the text like this. For example: “Select System info from the Administration panel.”

Warnings or important notes appear like this.

Tips and tricks appear like this.