To get the most out of this book

Data scientists, machine learning engineers, and AI researchers looking for quick solutions to different problems in reinforcement learning will find this book useful. Prior exposure to machine learning concepts is required, while previous experience with PyTorch is not required but will be a bonus.

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:

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The code bundle for the book is also hosted on GitHub at https://github.com/PacktPublishing/PyTorch-1.x-Reinforcement-Learning-Cookbook. 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://static.packt-cdn.com/downloads/9781838551964_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: "By saying empty, it doesn't mean all elements have a value of Null."

A block of code is set as follows:

>>> def random_policy():
...     action = torch.multinomial(torch.ones(n_action), 1).item()
...     return action

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

conda install pytorch torchvision -c pytorch

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: "This approach is called random search, since the weight is randomly picked in each trial with the hope that the best weight will be found with a large number of trials."

Warnings or important notes appear like this.

Tips and tricks appear like this.