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

23. Other Books You May Enjoy

References

Bouneffouf, D., & Rish, I. (2019). A Survey on Practical Applications of Multi-Armed and Contextual Bandits. Retrieved from arXiv: https://arxiv.org/abs/1904.10040
Chandrashekar, A., Amat, F., Basilico, J., & Jebara, T. (2017, December 7). Netflix Technology Blog. Retrieved from Artwork Personalization at Netflix: https://netflixtechblog.com/artwork-personalization-c589f074ad76
Chapelle, O., & Li, L. (2011). An Empirical Evaluation of Thompson Sampling. Advances in Neural Information Processing Systems 24, (pp. 2249-2257).
Collier, M., & Llorens, H. U. (2018). Deep Contextual Multi-Armed Bandits. Retrieved from arXiv: https://arxiv.org/abs/1807.09809
Gal, Y., Hron, J., & Kendall, A. (2017). Concrete Dropout. Advances in Neural Information Processing Systems 30, (pp. 3581-3590).
Marmerola, G. D. (2017, November 28). Thompson Sampling for Contextual bandits. Retrieved from Guilherme's Blog: https://gdmarmerola.github.io/ts...