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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Starting with Markov chains

We start this chapter with Markov chains, which do not involve any decision-making. They only model a special type of stochastic processes that are governed by some internal transition dynamics. Therefore, we don't talk about an agent yet. Understanding how Markov chains work will allow us to lay the foundation for MDPs that we will cover later.

Stochastic processes with Markov property

We already defined the state as the set information that completely describes the situation an environment is in. If the next state that the environment will transition into only depends on the current state, not the past, we say that the process has the Markov property. This is named after the Russian mathematician Andrey Markov.

Imagine a broken robot that randomly moves in a grid world. At any given step, the robot goes up, down, left and right with 0.2, 0.3, 0.25 and 0.25 probability, respectively. This is depicted in Figure 4.1, as follows:

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