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Mastering Reinforcement Learning with Python

You're reading from   Mastering Reinforcement Learning with Python Build next-generation, self-learning models using reinforcement learning techniques and best practices

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Product type Paperback
Published in Dec 2020
Publisher Packt
ISBN-13 9781838644147
Length 544 pages
Edition 1st Edition
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Author (1):
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Enes Bilgin Enes Bilgin
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Enes Bilgin
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Table of Contents (24) Chapters Close

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

Preface

Reinforcement Learning (RL) is a field of artificial intelligence used for creating self-learning autonomous agents. On a strong theoretical foundation, this book takes a pragmatic approach and uses examples inspired by real-world industry problems to teach you about state-of-the-art RL.

Starting with bandit problems, Markov decision processes, and dynamic programming, the book provides an in-depth review of the classical RL techniques, such Monte Carlo methods and temporal-difference learning. After that, you will learn about deep Q-learning, policy gradient algorithms, actor-critic methods, model-based methods, and multi-agenst reinforcement learning. Then, the book will introduce you to some of the key approaches behind the most successful RL implementations, such as domain randomization and curiosity-driven learning.

As you advance, you’ll delve into many novel algorithms with advanced implementations using modern Python libraries such as TensorFlow and Ray/RLlib. You’ll also find out how to implement RL in areas such as robotics, supply chain management, marketing, finance, smart cities, and cybersecurity while assessing the tradeoffs between different approaches and avoiding common pitfalls.

By the end of this reinforcement learning book, you’ll have mastered how to train and deploy your own RL agents for solving RL problems.

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