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In this paper we study a class of time-inconsistent terminal Markoviancontrol problems in discrete time subject to model uncertainty. We combine theconcept of the sub-game perfect strategies with the adaptive robust stochasticto tackle the theoretical aspects of the considered stochastic control problem.Consequently, as an important application of the theoretical results, byapplying a machine learning algorithm we solve numerically the mean-varianceportfolio selection problem under the model uncertainty.

Time-inconsistent Markovian control problems under model uncertainty with application to the mean-variance portfolio selection