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A0200
Title: Does the Markov decision process fit the data: Testing for the Markov property in sequential decision making Authors:  Chengchun Shi - LSE (United Kingdom) [presenting]
Runzhe Wan - NC State University (United States)
Rui Song - North Carolina State University (United States)
Wenbin Lu - NC State University (United States)
Ling Leng - Amazon (United States)
Abstract: The Markov assumption (MA) is fundamental to the empirical validity of reinforcement learning. We propose a novel Forward-Backward Learning procedure to test MA in sequential decision making. The proposed test does not assume any parametric form on the joint distribution of the observed data and plays an important role for identifying the optimal policy in high-order Markov decision processes and partially observable MDPs. We apply our test to both synthetic datasets and a real data example from mobile health studies to illustrate its usefulness.