  Offline estimation of the dynamical model of a Markov Decision Process (MDP)
is a non-trivial task that greatly depends on the data available in the
learning phase. Sometimes the dynamics of the model is invariant with respect
to some transformations of the current state and action. Recent works showed
that an expert-guided pipeline relying on Density Estimation methods as Deep
Neural Network based Normalizing Flows effectively detects this structure in
deterministic environments, both categorical and continuous-valued. The
acquired knowledge can be exploited to augment the original data set, leading
eventually to a reduction in the distributional shift between the true and the
learned model. Such data augmentation technique can be exploited as a
preliminary process to be executed before adopting an Offline Reinforcement
Learning architecture, increasing its performance. In this work we extend the
paradigm to also tackle non-deterministic MDPs, in particular, 1) we propose a
detection threshold in categorical environments based on statistical distances,
and 2) we show that the former results lead to a performance improvement when
solving the learned MDP and then applying the optimized policy in the real
environment.
