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 13: Exploring Advanced Topics

In this chapter, we cover several advanced topics in reinforcement learning. First of all, we go deeper into distributed reinforcement learning, in addition to our discussion in the previous chapters, which is a key topic to create scalable training architectures. Next, we present curiosity-driven reinforcement learning to handle hard-exploration problems that are not solvable by traditional exploration techniques. Finally, we discuss offline reinforcement learning, which leverages offline datasets rather than environment interactions to obtain good policies. All of these are hot research areas that you will hear more about over the next several years.

So, in this chapter, you will learn about the following:

  • Diving deeper into distributed reinforcement learning
  • Exploring curiosity-driven reinforcement learning
  • Offline reinforcement learning

Let's get started!