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

Other applications of multi-armed and contextual bandits

So far, we have focused on online advertising example as our running example. If you are wondering how commonly bandit algorithms are used in practice for such problems, it is actually quite common. For example, Microsoft has a service, called Personalizer, based on bandit algorithms (disclaimer: the author is a Microsoft employee at the time of writing this book). The example here itself is inspired by the work at Hubspot – a marketing solutions company (Collier & Llorens, 2018). Moreover, bandit problems have a vast array of practical applications other than advertising. In this section we briefly go over some of those applications.

Recommender systems

The bandit problems we formulated and solved in this chapter are a type of recommender system: they recommend which ad to display, potentially leveraging the information available about the users. There are many other recommender systems that use bandits in a...