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