To get the most out of this book

Working knowledge of Python is necessary. Knowledge of RL and the various tools used for it will also be beneficial.

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.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.packt.com.
2. Select the Support tab.
3. Click on Code Downloads.
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-Algorithms-with-Python. 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/9781789131116_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: "In this book, we use Python 3.7, but all versions above 3.5 should work. We also assume that you've already installed numpy and matplotlib."

A block of code is set as follows:

import gym

# create the environment 
env = gym.make("CartPole-v1")
# reset the environment before starting
env.reset()

# loop 10 times
for i in range(10):
    # take a random action
    env.step(env.action_space.sample())
    # render the game
   env.render()

# close the environment
env.close()

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

$ git clone https://github.com/pybox2d/pybox2d$ cd pybox2d
$ pip install -e .

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: "In reinforcement learning (RL), the algorithm is called the agent, and it learns from the data provided by an environment."

Warnings or important notes appear like this.

Tips and tricks appear like this.