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

23. Other Books You May Enjoy

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Summary

In this chapter, we covered three important approaches to solving MDPs: Dynamic programming, Monte Carlo methods, and temporal-difference learning. We have seen that while DP provides exact solutions to MDPs, it requires knowing the precise dynamics of an environment. Monte Carlo and TD learning methods, on the other hand, explore in the environment and learn from experience. TD learning, in particular, can learn from even a single step transitions in the environment. Within the chapter, we also presented on-policy methods, which estimate the value functions for a behavior policy, while off-policy methods for a target policy. Finally, we discussed the importance of the simulator in RL experiments and what to pay attention to when working with one.

Next, we take our journey to a next level and dive into deep reinforcement learning, which will enable us to solve some complex real-world problems. Particularly, in the next chapter, we cover deep Q-learning in detail.

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