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Python Reinforcement Learning Projects

You're reading from   Python Reinforcement Learning Projects Eight hands-on projects exploring reinforcement learning algorithms using TensorFlow

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Product type Paperback
Published in Sep 2018
Publisher Packt
ISBN-13 9781788991612
Length 296 pages
Edition 1st Edition
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Authors (3):
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Sean Saito Sean Saito
Author Profile Icon Sean Saito
Sean Saito
Rajalingappaa Shanmugamani Rajalingappaa Shanmugamani
Author Profile Icon Rajalingappaa Shanmugamani
Rajalingappaa Shanmugamani
Yang Wenzhuo Yang Wenzhuo
Author Profile Icon Yang Wenzhuo
Yang Wenzhuo
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Toc

To get the most out of this book

The examples covered in this book can be run on Windows, Ubuntu, or macOS. All the installation instructions are covered. A basic knowledge of Python and machine learning is required. It's preferred that you have GPU hardware, but it's not necessary.

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/Python-Reinforcement-Learning-ProjectsIn 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!

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 gym-minecraft package has the same interface as other Gym environments."

A block of code is set as follows:

import logging
import minecraft_py
logging.basicConfig(level=logging.DEBUG)

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

python3 -m pip install gym
python3 -m pip install pygame

Bold: Indicates a new term, an important word, or words that you see on screen. 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.
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