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The Markov assumption (MA) is fundamental to the empirical validity ofreinforcement learning. In this paper, we propose a novel Forward-BackwardLearning procedure to test MA in sequential decision making. The proposed testdoes not assume any parametric form on the joint distribution of the observeddata and plays an important role for identifying the optimal policy inhigh-order Markov decision processes and partially observable MDPs. We applyour test to both synthetic datasets and a real data example from mobile healthstudies to illustrate its usefulness.

Does the Markov Decision Process Fit the Data: Testing for the Markov Property in Sequential Decision Making