Getting Started with OpenAI Gym and Deep Reinforcement Learning

The introduction chapters gave you a good insight into the OpenAI Gym toolkit and reinforcement learning in general. In this chapter, we will jump right in and get you and your computer ready with all the required preparation, installations, and configurations to start developing your agents. More importantly, you will also find instructions to access the book's code repositories, which contain all the code you will need to follow this book in its entirety, along with several other code examples, useful instructions, and updates.

In this chapter, we will cover the following topics:

• Accessing the code repository for this book
• Creating an Anaconda environment for working through this book
• How to install and configure OpenAI Gym and dependencies on your system
• Installing tools, libraries, and dependencies for deep...

Code repository, setup, and configuration

First of all, let's make sure you have all the information to access the code repository for this book. The source code provides you with all the necessary code samples that we will discuss in this book and provides additional details on how to set up and run the training or testing scripts for each chapter specifically. To get started, head to the book's code repository on GitHub at the following link: https://github.com/PacktPublishing/Hands-On-Intelligent-Agents-with-OpenAI-Gym.

Create a GitHub account if you do not already have one and fork the repository so that it is added to your own GitHub account. This is recommended as it allows you to make any changes to the code you prefer while following along, and also allow you to send a pull request when you have something cool to show and be featured on the book's blog!

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Installing tools and libraries needed for deep reinforcement learning

Chapter 2, Reinforcement Learning and Deep Reinforcement Learning, prepped you with the basics of reinforcement learning. With that theoretical background, we will be able to implement some cool algorithms. Before that, we will make sure we have the required tools and libraries at our disposal.

We can actually write cool reinforcement learning algorithms in Python without using any higher-level libraries. However, when we start to use function approximators for the value functions or the policy, and especially if we use deep neural networks as the function approximators, it is better to use highly optimized deep learning libraries instead of writing our own routines. A deep learning library is the major tool/library that we will need to install. There are different libraries out there today: PyTorch, TensorFlow...

Summary

In this chapter, we went through the step-by-step setup process to install and configure our development environment using conda, OpenAI Gym, and Pytorch! This chapter helped us make sure that we have all the required tools and libraries installed to start developing our agents in Gym environments. In the next chapter, we will explore the features of Gym environments to understand how they work, and how we can use them to train our agents. In Chapter 5, Implementing Your First Learning Agent – Solving the Mountain Car Problem, we will jump right into developing our first reinforcement learning agent to solve the mountain car problem! We will then gradually move on and implement more sophisticated reinforcement learning algorithms in the subsequent chapters.