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

Developing effective marketing strategies using reinforcement learning

Reinforcement learning can significantly improve marketing strategies in multiple areas. Let's now talk about some of them.

Personalized marketing content

In relation to the previous section, there is always room for more personalization in marketing. Rather than sending the same email or flier to all customers, or having rough customer segmentations, reinforcement learning can help determining the best sequence of personalized marketing content to communicate to individual customers.

Marketing resource allocation for customer acquisition

Marketing departments often make the decisions about where to spend the budget based on subjective judgements and/or simple models. RL can actually come up with pretty dynamic policies to allocate marketing resources, while leveraging the information about the product, responses from different marketing channels, and context information such as time of the year...