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)
1
Section 1: Reinforcement Learning Foundations
7
Section 2: Deep Reinforcement Learning
12
Section 3: Advanced Topics in RL
17
Section 4: Applications of RL

Chapter 5: Solving the Reinforcement Learning Problem

In the previous chapter we provided the mathematical foundations for modeling a reinforcement learning problem. In this chapter, we lay the foundation for solving it. Many of the following chapters will focus on some specific solution approaches that will rise on this foundation. To this end, we first cover the dynamic programming (DP) approach, with which we introduce some key ideas and concepts. DP methods provide optimal solutions to Markov decision processes (MDPs), yet they require the complete knowledge and a compact representation of the state transition and reward dynamics of the environment. This could be severely limiting and impractical in a realistic scenario, where the agent is either directly trained in the environment itself or in a simulation of it. Monte Carlo and temporal difference (TD) approaches that we cover later, unlike DP, use sampled transitions from the environment and relax the aforementioned limitations...