Chapter 1: Introduction to Reinforcement Learning

Reinforcement Learning (RL) aims to create Artificial Intelligence (AI) agents that can make decisions in complex and uncertain environments, with the goal of maximizing their long-term benefit. These agents learn how to do it through interacting with their environments, which mimics the way we as humans learn from experience. As such, RL has an incredibly broad and adaptable set of applications, with the potential to disrupt and revolutionize global industries.

This book will give you an advanced level understanding of this field. We will go deeper into the theory behind the algorithms you may already know, and cover state-of-the art RL. Moreover, this is a practical book. You will see examples inspired by real-world industry problems and learn expert tips along the way. By its conclusion, you will be able to model and solve your own sequential decision-making problems using Python.

So, let's start our journey with refreshing your mind on RL concepts and get you set up for the advanced material upcoming in the following chapters. Specifically, this chapter covers:

• Why reinforcement learning?
• The three paradigms of ML
• RL application areas and success stories
• Elements of a RL problem
• Setting up your RL environment