Book Image

Mastering Reinforcement Learning with Python

By : Enes Bilgin
Book Image

Mastering Reinforcement Learning with Python

By: Enes Bilgin

Overview of this book

Reinforcement learning (RL) is a field of artificial intelligence (AI) used for creating self-learning autonomous agents. Building on a strong theoretical foundation, this book takes a practical approach and uses examples inspired by real-world industry problems to teach you about state-of-the-art RL. Starting with bandit problems, Markov decision processes, and dynamic programming, the book provides an in-depth review of the classical RL techniques, such as Monte Carlo methods and temporal-difference learning. After that, you will learn about deep Q-learning, policy gradient algorithms, actor-critic methods, model-based methods, and multi-agent reinforcement learning. Then, you'll be introduced to some of the key approaches behind the most successful RL implementations, such as domain randomization and curiosity-driven learning. As you advance, you’ll explore many novel algorithms with advanced implementations using modern Python libraries such as TensorFlow and Ray’s RLlib package. You’ll also find out how to implement RL in areas such as robotics, supply chain management, marketing, finance, smart cities, and cybersecurity while assessing the trade-offs between different approaches and avoiding common pitfalls. By the end of this book, you’ll have mastered how to train and deploy your own RL agents for solving RL problems.
Table of Contents (24 chapters)
Section 1: Reinforcement Learning Foundations
Section 2: Deep Reinforcement Learning
Section 3: Advanced Topics in RL
Section 4: Applications of RL

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