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

Optimizing inventory procurement decisions

One of the most important decisions that almost all manufacturers, distributors, and retailers need to make, all the time, is how much inventory to carry to reliably satisfy the customer demand while minimizing the costs. Effective inventory management is key to the profitability and survival of most companies, especially given the razor-thin margins and increased customer expectations in today's competitive landscape. In this section, we use reinforcement learning to address this challenge and optimize inventory procurement decisions.

The need for inventory and the trade off in its management

When you walk into a supermarket, you see items stacked on top of each other. There are probably more of those items in the depot of the supermarket, and more at the warehouse of the distributors, and more at the sites of the manufacturers. If you think about it, there are those huge piles of products just sitting somewhere, waiting to be...