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You're reading from  Mastering Reinforcement Learning with Python

Product typeBook
Published inDec 2020
Reading LevelBeginner
PublisherPackt
ISBN-139781838644147
Edition1st Edition
Languages
Python
Tools
PyTorch
Concepts
Reinforcement Learning
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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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Using function approximation for context

Function approximations allow us to model the dynamics of a process from which we have observed data, such as contexts and ad clicks. As in the previous chapter, consider an online advertising scenario with five different ads (i.e. A, B, C, D, and E), with the context comprised of user device, location and age. In this section, our agent will learn five different Q functions, one per ad, each receiving a context , and return the action value estimate. This is illustrated in Figure 1.

Figure 3.1 – We learn a function for each action that receives the context and returns the action value.

At this point, we have a supervised machine learning problem to solve for each action. We can use different models to obtain the Q functions, such as logistic regression or a neural network (which actually allows us to use a single network that estimates values for all actions). Once we choose the type of function approximation...

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

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