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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Introducing model-based methods

Imagine a scene in which you are traveling in a car on an undivided road and you face the following situation. Suddenly, another car in the opposing direction approaches you fast in your lane as it is passing a truck. Chances are your mind automatically simulates different scenarios about how the next scenes might unfold:

The other car might go back to its lane right away or drive even faster to pass the truck as soon as possible.
Another scenario could be the car steering toward your right, but this is an unlikely scenario (in a right-hand traffic flow).

The driver (possibly you) then evaluates the likelihood and risk of each scenario, together with their possible actions too, and makes the decision to safely continue the journey.

In a less sensational example, consider a game of chess. Before making a move, a player "simulates" many scenarios in their head and assesses the possible outcomes of several moves down...