You're reading from Mastering Reinforcement Learning with Python Build next-generation, self-learning models using reinforcement learning techniques and best practices

Product type Paperback

Published in Dec 2020

Publisher Packt

ISBN-13 9781838644147

Length 544 pages

Edition 1st Edition

Languages

Python

Tools

PyTorch

Concepts

Reinforcement Learning

Author (1):

Enes Bilgin

View More author details

Table of Contents (24) Chapters

Preface

1. Section 1: Reinforcement Learning Foundations

2. Chapter 1: Introduction to Reinforcement Learning FREE CHAPTER

3. Chapter 2: Multi-Armed Bandits

4. Chapter 3: Contextual Bandits

5. Chapter 4: Makings of a Markov Decision Process

6. Chapter 5: Solving the Reinforcement Learning Problem

7. Section 2: Deep Reinforcement Learning

8. Chapter 6: Deep Q-Learning at Scale

9. Chapter 7: Policy-Based Methods

10. Chapter 8: Model-Based Methods

11. Chapter 9: Multi-Agent Reinforcement Learning

12. Section 3: Advanced Topics in RL

13. Chapter 10: Introducing Machine Teaching

14. Chapter 11: Achieving Generalization and Overcoming Partial Observability

15. Chapter 12: Meta-Reinforcement Learning

16. Chapter 13: Exploring Advanced Topics

17. Section 4: Applications of RL

18. Chapter 14: Solving Robot Learning

19. Chapter 15: Supply Chain Management

20. Chapter 16: Personalization, Marketing, and Finance

21. Chapter 17: Smart City and Cybersecurity

22. Chapter 18: Challenges and Future Directions in Reinforcement Learning

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Exploring dynamic programming

Dynamic programming is a branch of mathematical optimization that proposes optimal solution methods to MDPs. Although most real-world problems are too complex to optimally solve via DP methods, the ideas behind these algorithms are central to many RL approaches. So, it is important to have a solid understanding of them. Throughout this chapter, we go from these exact methods to more practical approaches by systematically introducing approximations.

We start this section by describing an example that will serve as a use case for the algorithms that we will introduce throughout the chapter. Then, we will cover how to do prediction and control using DP. Let's get started!

Example use case: Inventory replenishment of a food truck

Our use case involves a food truck business that needs to decide how many burger patties to buy every weekday to replenish its inventory. Inventory planning is an important class of problems in retail and manufacturing...