To get the most out of this book

In this book, reinforcement learning algorithms are implemented in Python. To reproduce the many examples in this book, you need to possess a good knowledge of the Python environment. We have used Python 3.6 and above to build various applications. In that spirit, we have tried to keep all of the code as friendly and readable as possible. We feel that this will enable you to easily understand the code and readily use it in different scenarios.

Download the example code files

You can download the example code files for this book from your account at www.packt.com. If you purchased this book elsewhere, you can visit www.packt.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.packt.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/Keras-Reinforcement-Learning-Projects. 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: https://www.packtpub.com/sites/default/files/downloads/9781789342093_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: "To calculate the logarithm of returns, we will use the log() function from numpy."

A block of code is set as follows:

plt.figure(figsize=(10,5))
plt.plot(dataset)
plt.show()

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

git clone https://github.com/openai/gym
cd gym
pip install -e .

Warnings or important notes appear like this.

Tips and tricks appear like this.