  Appropriate credit assignment for delay rewards is a fundamental challenge
for reinforcement learning. To tackle this problem, we introduce a delay reward
calibration paradigm inspired from a classification perspective. We hypothesize
that well-represented state vectors share similarities with each other since
they contain the same or equivalent essential information. To this end, we
define an empirical sufficient distribution, where the state vectors within the
distribution will lead agents to environmental reward signals in the consequent
steps. Therefore, a purify-trained classifier is designed to obtain the
distribution and generate the calibrated rewards. We examine the correctness of
sufficient state extraction by tracking the real-time extraction and building
different reward functions in environments. The results demonstrate that the
classifier could generate timely and accurate calibrated rewards. Moreover, the
rewards are able to make the model training process more efficient. Finally, we
identify and discuss that the sufficient states extracted by our model resonate
with the observations of humans.
