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

Exploring dynamic programming

Dynamic programming is a branch of mathematical optimization that proposes optimal solution methods to MDPs. Although most real-world problems are too complex to optimally solve via DP methods, the ideas behind these algorithms are central to many RL approaches. So, it is important to have a solid understanding of them. Throughout this chapter, we go from these exact methods to more practical approaches by systematically introducing approximations.

We start this section by describing an example that will serve as a use case for the algorithms that we will introduce throughout the chapter. Then, we will cover how to do prediction and control using DP. Let's get started!

Example use case: Inventory replenishment of a food truck

Our use case involves a food truck business that needs to decide how many burger patties to buy every weekday to replenish its inventory. Inventory planning is an important class of problems in retail and manufacturing...