  This position paper proposes a fresh look at Reinforcement Learning (RL) from
the perspective of data-efficiency. Data-efficient RL has gone through three
major stages: pure on-line RL where every data-point is considered only once,
RL with a replay buffer where additional learning is done on a portion of the
experience, and finally transition memory based RL, where, conceptually, all
transitions are stored and re-used in every update step. While inferring
knowledge from all explicitly stored experience has lead to a tremendous gain
in data-efficiency, the question of how this data is collected has been vastly
understudied. We argue that data-efficiency can only be achieved through
careful consideration of both aspects. We propose to make this insight explicit
via a paradigm that we call 'Collect and Infer', which explicitly models RL as
two separate but interconnected processes, concerned with data collection and
knowledge inference respectively. We discuss implications of the paradigm, how
its ideas are reflected in the literature, and how it can guide future research
into data efficient RL.
