Deep Reinforcement Learning has shown significant progress in extracting useful representations from high-dimensional inputs albeit using handcrafted auxiliary tasks and pseudo rewards. Automatically learning such representations in an object-oriented manner geared towards control and fast adaptation remains an open research problem. We introduce a method that tries to discover meaningful features using slot-attention, translating them to temporally coherent ‘question’ functions and leveraging t…