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 covered two important classes of problems in supply chain: Inventory optimization and vehicle routing. These are both very complex problems, and reinforcement learning has recently emerged as a competitive tool to address them. In the chapter, for the former problem, we provided you with a detailed discussion on how the create the environment and solve the corresponding reinforcement learning problem. The challenge in this problem was the high variance across episodes, which we mitigated through a careful hyperparameter tuning procedure. For the latter problem, we described a realistic case of a gig driver who delivers meal orders that dynamically arrive from customers. We discussed how the model can be made more flexible to work with a varying size of nodes via pointer networks.

In the next chapter, we will discuss yet another exciting set of applications around personalization, marketing, and finance. See you there!