To get the most out of this book

All chapters in the book describing RL methods have the same structure: in the beginning we discuss the motivation of the method, its theoretical foundation, and intuition behind it. Then, we follow several examples of the method applied to different environment with full source code. So, you can use the book in different ways:

1. To quickly become familiar with some method of methods you can read only introductory part of the relevant chapter or chapter's section.
2. To get deeper understanding of the way method is implemented you can read the code and the comments around.
3. To gain deep familiarity with the method (the best way to learn, I believe) you should try to reimplement the method and make it working, using 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 http://www.packtpub.com. If you purchased this book elsewhere, you can visit http://www.packtpub.com/support 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 http://www.packtpub.com.
2. Select the SUPPORT tab.
3. Click on Code Downloads & Errata.
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 https://github.com/PacktPublishing/Deep-Reinforcement-Learning-Hands-On. We also have other code bundles from our rich catalog of books and videos available at https://github.com/PacktPublishing/. 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: https://www.packtpub.com/sites/default/files/downloads/DeepReinforcementLearningHandsOn_ColorImages.pdf.

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; "The method get_observation() is supposed to return to the agent the current environment's observation."

A block of code is set as follows:

    def get_actions(self):
        return [0, 1]

When we wish to draw your attention to a particular part of a code block, the relevant lines or items are set in bold:

    def get_actions(self):
        return [0, 1]

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

$ xvfb-run -s "-screen 0 640x480x24" python 04_cartpole_random_monitor.py

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: "In practice it's some piece of code, which implements some policy."