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

Going beyond bandits for personalization

When we covered multi-armed and contextual bandit problems in the early chapters of the book, we presented a case study that aimed maximizing the click-through rate (CTR) of online ads. This is just one example of how bandit models can be used to provide users with personalized content and experience, a common challenge of almost all online (and offline) content providers, from e-retailers to social media platforms. In this section, we go beyond the bandit models and describe a multi-step reinforcement learning approach to personalization. Let's first start with discussing where the bandit models fall short, and then how multi-step RL can address those issues.

Shortcomings of bandit models

The goal in bandit problems is to maximize the immediate (single step) return. In an online ad CTR maximization problem, this is usually a good way of thinking about the goal: An ad is displayed, the user has clicked, and voila! If not, it&apos...