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You're reading from  Mastering Reinforcement Learning with Python

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Published inDec 2020
Reading LevelBeginner
PublisherPackt
ISBN-139781838644147
Edition1st Edition
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Author (1)
Enes Bilgin
Enes Bilgin
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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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Summary

In this chapter, we've concluded our discussion on bandit problems with contextual bandits. As we mentioned, bandit problems have many practical applications. So, it would not be a surprise if you already had a problem in your business or research that can be modeled as a bandit problem. Now that you know how to formulate and solve one, go out and apply what you have learned! Bandit problems are also important to develop intuition on how to solve exploration-exploitation dilemma, which will exist in almost every RL setting.

Now that you have a solid understanding of how to solve one-step RL, it is time to move on to full-blown multi-step RL. In the next chapter, we will go into the theory behind multi-step RL with Markov Decision Processes, and build the foundation for modern deep RL methods that we will cover in the subsequent chapters.

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Mastering Reinforcement Learning with Python
Published in: Dec 2020Publisher: PacktISBN-13: 9781838644147

Author (1)

author image
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.
Read more about Enes Bilgin