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


In this chapter, we've concluded our discussion on bandit problems with contextual bandits. As we mentioned, bandit problems have many practical applications. So, it would not be a surprise if you already had a problem in your business or research that can be modeled as a bandit problem. Now that you know how to formulate and solve one, go out and apply what you have learned! Bandit problems are also important to develop intuition on how to solve exploration-exploitation dilemma, which will exist in almost every RL setting.

Now that you have a solid understanding of how to solve one-step RL, it is time to move on to full-blown multi-step RL. In the next chapter, we will go into the theory behind multi-step RL with Markov Decision Processes, and build the foundation for modern deep RL methods that we will cover in the subsequent chapters.