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 16: Personalization, Marketing, and Finance

In this chapter, we discuss three areas in which reinforcement learning is gaining significant traction. First, we describe how it can be used in personalization and recommendation systems. With that, we go beyond the single-step bandit approaches we covered in the earlier chapters. A related field that can also significantly benefit from reinforcement learning is marketing. In addition to personalized marketing applications, reinforcement learning can help in areas like managing campaign budgets and reducing customer churn. Finally, we discuss the promise of RL in finance and the related challenges. In doing so, we introduce TensorTrade, a Python framework for developing and testing RL-based trading algorithms.

So, in this chapter, we cover:

  • Going beyond bandits for personalization,
  • Developing effective marketing strategies using reinforcement learning,
  • Applying reinforcement learning in finance.