Activity Number:
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83
- Your Invited Poster Evening Entertainment: No Longer Board
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Type:
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Invited
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Date/Time:
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Sunday, July 30, 2017 : 8:30 PM to 10:30 PM
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Sponsor:
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Section on Statistical Learning and Data Science
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Abstract #323040
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Title:
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Sufficient Markov Decision Processes with Alternating Deep Neural Networks
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Author(s):
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Longshaokan Wang* and Eric Laber and Katie Witkiewitz
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Companies:
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North Carolina State University and North Carolina State University and University of New Mexico
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Keywords:
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Markov Decision Processes ;
Dimension Reduction ;
Deep Learning ;
Reinforcement Learning ;
Mobile Health ;
Sufficient Statistics
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Abstract:
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Advances in mobile computing technologies have made it possible to monitor and apply data-driven interventions across complex systems in real time. Markov decision processes (MDPs) are the primary model for sequential decision problems with a large or indefinite time horizon. Choosing a representation of the underlying decision process that is both Markov and low-dimensional is non-trivial. We propose a method for constructing a low-dimensional representation of the original decision process for which: 1. the MDP model holds; 2. a decision strategy that maximizes mean utility when applied to the low-dimensional representation also maximizes mean utility when applied to the original process. We use a deep neural network to define a class of potential process representations and estimate the process of lowest dimension within this class. The method is illustrated using data from a mobile study on heavy drinking and smoking among college students.
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Authors who are presenting talks have a * after their name.