Sufficient Markov Decision Processes with Alternating Deep Neural Networks

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ID: 282114
2017
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Abstract
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.
Reference Key
witkiewitz2017sufficient Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Longshaokan Wang; Eric B. Laber; Katie Witkiewitz
Journal arXiv
Year 2017
DOI
DOI not found
URL
http://arxiv.org/abs/1704.07531v2
Keywords
stat.me stat.ml math.st stat.th

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