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Mastering Reinforcement Learning with Python

You're reading from Mastering Reinforcement Learning with Python

Product type Book

Published in Dec 2020

Publisher Packt

ISBN-13 9781838644147

Pages 544 pages

Edition 1st Edition

Languages

Concepts

Reinforcement Learning

Author (1):

Enes Bilgin

Table of Contents (24) Chapters

Preface

1. Section 1: Reinforcement Learning Foundations

2. Chapter 1: Introduction to Reinforcement Learning

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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How to pick the right algorithm?

As in all machine learning domains, there is no silver bullet in terms of which algorithm to use for different applications. There are many criteria you should consider, and in some cases some of them will be more important than others.

Here are different dimensions of algorithm performances that you should look into when picking your algorithm.

Highest reward: When you are not bounded by compute and time resources and your goals is simply to train the best possible agent for your application, highest reward is the criterion you should pay attention to. PPO and SAC are promising alternatives here.
Sample efficiency: If your sampling process is costly / time-consuming, then sample efficiency (achieving higher rewards using less samples is important). When this is the case, you should look into off-policy algorithms as they reuse past experiences for training as on-policy methods are often incredibly wasteful in how they consume samples...

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Authors (1)

Enes Bilgin

Enes Bilgin works as a senior AI engineer and a tech lead in Microsoft's Autonomous Systems division. He is a machine learning and operations research practitioner and researcher with experience in building production systems and models for top tech companies using Python, TensorFlow, and Ray/RLlib. He holds an M.S. and a Ph.D. in systems engineering from Boston University and a B.S. in industrial engineering from Bilkent University. In the past, he has worked as a research scientist at Amazon and as an operations research scientist at AMD. He also held adjunct faculty positions at the McCombs School of Business at the University of Texas at Austin and at the Ingram School of Engineering at Texas State University.

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