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

Gudimella, Aditya, et al. Deep Reinforcement Learning for Dexterous Manipulation with Concept Networks. arXiv.org, http://arxiv.org/abs/1709.06977.
Bonsai. (2017). Deep Reinforcement Learning Models: Tips & Tricks for Writing Reward Functions. Medium. URL: https://bit.ly/33eTjBv
Bonsai. (2017). Writing Great Reward Functions – Bonsai. YouTube. URL: https://youtu.be/0R3PnJEisqk
Weng, L. (2020). Curriculum for Reinforcement Learning. Lil'Log. URL: https://bit.ly/39foJvE
OpenAI. (2016). Faulty Reward Functions in the Wild. OpenAI Blog. URL: https://openai.com/blog/faulty-reward-functions/
Irpan, A. (2018). Deep Reinforcement Learning Doesn't Work Yet. Sorta Insightful. URL: https://www.alexirpan.com/2018/02/14/rl-hard.html
Heess, Nicolas, et al. (2017). Emergence of Locomotion Behaviours in Rich Environments. arXiv.org. URL: http://arxiv.org/abs/1707.02286
Badnava, Babak, and Nasser Mozayani. (2019). A New Potential-Based...