The following is a short list of the tools and attributes that may make you more successful as you explore this book:

• Computer: A desktop computer capable of running Unity, but all the samples are basic enough that even a low-end machine should be sufficient. Check the Unity documentation for the minimum requirements to run Unity.
• Patience: You may need to train agents for several hours, so expect to wait. Just remember that your patience will be rewarded (Alton Brown). The better your machine, the less you wait, so there's also that.
• GPU: Don't fret if your computer does not have a support GPU to run TensorFlow; you can run the samples without it. It is nice to have, though.
• High-school math: If you need to brush up, basic statistics, algebra, and geometry should be sufficient. Developing your own apps will certainly benefit from a better understanding of the mathematics.
• Programming: A basic understanding of C# is required. You will find it helpful if you also know Unity and Python, but this is not required to run the exercises.

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/Learn-Unity-ML-Agents-Fundamentals-of-Unity-Machine-Learning. 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!

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/LearnUnityMLAgentsFundamentalsofUnityMachineLearning_ColorImages.pdf.

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: " This command will create a new 3.5 Python environment named [-n] mlagents. "

A block of code is set as follows:

 void Defend()
      {
        currentAction = "Defend";
        nextAction = Time.timeSinceLevelLoad + (25 / MaxSpeed);
      }

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

 conda activate ml-agents

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."