  Even though AI has advanced rapidly in recent years displaying success in
solving highly complex problems, the class of Bongard Problems (BPs) yet remain
largely unsolved by modern ML techniques. In this paper, we propose a new
approach in an attempt to not only solve BPs but also extract meaning out of
learned representations. This includes the reformulation of the classical BP
into a reinforcement learning (RL) setting which will allow the model to gain
access to counterfactuals to guide its decisions but also explain its
decisions. Since learning meaningful representations in BPs is an essential
sub-problem, we further make use of contrastive learning for the extraction of
low level features from pixel data. Several experiments have been conducted for
analyzing the general BP-RL setup, feature extraction methods and using the
best combination for the feature space analysis and its interpretation.
