To get the most out of this book

The following are the requirements to get the most out of this book:

• Python and TensorFlow
• Linear algebra as a prerequisite for neural networks
• Installation bundle: Python, TensorFlow, and OpenAI gym (shown in Chapter 1, Deep Learning – Architectures and Frameworks and Chapter 2, Training Reinforcement Learning Agents Using OpenAI Gym)

Download the example code files

You can download the example code files for this book from your account at www.packtpub.com. If you purchased this book elsewhere, you can visit 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 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 onscreen 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/Reinforcement-Learning-with-TensorFlow. 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 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: http://www.packtpub.com/sites/default/files/downloads/ReinforcementLearningwithTensorFlow_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. Here is an example: "The sigmoid(x) and relu(x) refer to the functions performing sigmoid and ReLU activation calculations respectively."

A block of code is set as follows:

def discretization(env, obs):
    
    env_low = env.observation_space.low
    env_high = env.observation_space.high

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

Episode 1 completed with total reward 8433.30289388 in 26839 steps
Episode 2 completed with total reward 3072.93369963 in 8811 steps
Episode 3 completed with total reward 1230.81734028 in 4395 steps
Episode 4 completed with total reward 2182.31111239 in 6629 steps

Bold: Indicates a new term, an important word, or words that you see onscreen. For example, words in menus or dialog boxes appear in the text like this. Here is an example: "Select System info from the Administration panel."

Warnings or important notes appear like this.

Tips and tricks appear like this.