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.

本文提出了一种基于对象的方法，试图从对象中发现有意义的特征，将其转化为具有时间相关性的 “指导” 函数，并利用随后学习到的一般价值函数进行控制，并且通过定性分析表明，学习到的表示不仅可解释而且围绕着任务之间不变的对象，从而促进了快速适应。