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 4: Makings of a Markov Decision Process

In the first chapter, we talked about many applications of Reinforcement Learning (RL), from robotics to finance. Before implementing any RL algorithms for such applications, we need to first model them mathematically. Markov Decision Process (MDP) is the framework we use to model such sequential decision-making problems. MDPs have some special characteristics that make it easier for us to theoretically analyze them. Building on that theory, Dynamic Programming (DP) is the field that proposes solution methods for MDPs. RL, in some sense, is a collection of approximate DP approaches, which enable us to obtain good (but not necessarily optimal) solutions to very complex problems that are intractable to solve with exact DP methods.

In this chapter we step-by-step build the MDP, explain its characteristics, and lay down the mathematical foundation for the RL algorithms upcoming in the later chapters. In an MDP, the actions an agent takes...