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

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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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Chapter 4: Makings of a Markov Decision Process

In the first chapter, we talked about many applications of Reinforcement Learning (RL), from robotics to finance. Before implementing any RL algorithms for such applications, we need to first model them mathematically. Markov Decision Process (MDP) is the framework we use to model such sequential decision-making problems. MDPs have some special characteristics that make it easier for us to theoretically analyze them. Building on that theory, Dynamic Programming (DP) is the field that proposes solution methods for MDPs. RL, in some sense, is a collection of approximate DP approaches, which enable us to obtain good (but not necessarily optimal) solutions to very complex problems that are intractable to solve with exact DP methods.

In this chapter we step-by-step build the MDP, explain its characteristics, and lay down the mathematical foundation for the RL algorithms upcoming in the later chapters. In an MDP, the actions an agent takes...