Introduction

In the previous chapter, you were introduced to TensorFlow and Keras, along with an overview of their key features and applications and how they work in synergy. You learned how to implement a deep neural network with TensorFlow, addressing all major topics, that is, model creation, training, validation, and testing, using the most advanced machine learning frameworks available. In this chapter, we will use this knowledge to build models that are able to solve some classical reinforcement learning problems.

Reinforcement learning is a branch of machine learning that comes closest to the idea of artificial intelligence. The goal of training an artificial system to learn a given task, without any prior information, and only by means of experiences of an environment, represents the ambitious aim of replicating human learning. Applying deep learning techniques to the field has recently led to a great increase in performance, thus allowing us to solve problems in very different domains, from classic control problems to video games and even robotic locomotion. This chapter will introduce the various resources, methods, and tools you can use to become familiar with the context and problems typically encountered when getting started in the field. In particular, we will look at OpenAI Gym and OpenAI Universe, two libraries that allow us to easily create environments where we can train Reinforcement Learning (RL) agents, and OpenAI Baselines, a tool that provides a clear and simple interface for state-of-the-art reinforcement learning algorithms. By the end of this chapter, you will be able to leverage top libraries and modules to easily train a state-of-the-art reinforcement learning agent to solve classic control problems, as well as to achieve better-than-human performance on classic video games.

Now, let's begin our journey, starting with the very first important concept: how to correctly model a proper reinforcement learning environment where we can train an agent. For this, we will be using OpenAI Gym and Universe.