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

Suggestions for aspiring reinforcement learning experts

This book is designed for an audience who already know the fundamentals of RL. Now that you have finished this book too, you are well positioned to become an expert in this field. Having said that, RL is big area; and this book is really meant to be a compass and kickstarter for you. At this point, if you decide to go deeper in RL, I will have some suggestions.

Go deeper into the theory

In machine learning, models often fail to produce expected level of performance, at least at the beginning. One big factor that will help you go beyond what comes out of the box is to have a good foundation of the math behind the algorithms you are using. This will help you better understand the limitations and assumptions of those algorithms, identify whether they conflict with the realities of the problem at hand, and give you ideas for addressing them. To this end, here is some advice:

  • It is never a bad idea to deepen your understanding...