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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
Author (1):
Enes Bilgin Enes Bilgin
Profile icon Enes Bilgin
LinkedIn
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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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

Summary

In this chapter, we have covered the mathematical framework in which we model the sequential decision-making problems we face in real-life: Markov decision processes. To this end, we started with Markov chains, which do not involve any concept of reward or decision making. Markov chains simply describe stochastic processes where the system transitions based on the current state and independent of the previously visited states. We then added the notion of a reward and started discussing things like which states are more advantageous to be in in terms of the expected future rewards. This created a concept of a "value" for a state. Then, we finally brought in the concept of "decision/action" and defined the Markov decision process. Subsequently, we finalized the definitions of state-value functions and action-value functions. Lastly, we discussed what a partially observable environment is and how it affects the decision-making of an agent.

The Bellman equation...

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Authors (1)

Profile icon Enes Bilgin
LinkedIn
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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