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

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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References

Levine, Sergey. (2019). Optimal Control and Planning. CS285 Fa19 10/2/19. YouTube. URL: https://youtu.be/pE0GUFs-EHI
Levine, Sergey. (2019). Model-Based Reinforcement Learning. CS285 Fa19 10/7/19. YouTube. URL: https://youtu.be/6JDfrPRhexQ
Levine, Sergey. (2019). Model-Based Policy Learning. CS285 Fa19 10/14/19. YouTube. URL: https://youtu.be/9AbBfIgTzoo.
Ha, David, and Jürgen Schmidhuber. (2018). World Models. arXiv.org, URL: https://arxiv.org/abs/1803.10122.
Mania, Horia, et al. (2018). Simple Random Search Provides a Competitive Approach to Reinforcement Learning. arXiv.org, URL: http://arxiv.org/abs/1803.07055
Jospin, Laurent Valentin, et al. (2020). Hands-on Bayesian Neural Networks – a Tutorial for Deep Learning Users. arXiv.org, http://arxiv.org/abs/2007.06823.
Joseph, Trist'n. (2020). Bootstrapping Statistics. What It Is and Why It's Used. Medium. URL: https://bit.ly/3fOlvjK.
Richard S. Sutton. (1991). Dyna...