  Deep Reinforcement Learning has shown significant progress in extracting
useful representations from high-dimensional inputs albeit using hand-crafted
auxiliary tasks and pseudo rewards. Automatically learning such representations
in an object-centric manner geared towards control and fast adaptation remains
an open research problem. In this paper, we introduce a method that tries to
discover meaningful features from objects, translating them to temporally
coherent "question" functions and leveraging the subsequent learned general
value functions for control. We compare our approach with state-of-the-art
techniques alongside other ablations and show competitive performance in both
stationary and non-stationary settings. Finally, we also investigate the
discovered general value functions and through qualitative analysis show that
the learned representations are not only interpretable but also, centered
around objects that are invariant to changes across tasks facilitating fast
adaptation.
