  It is well known that ensemble methods often provide enhanced performance in
reinforcement learning. In this paper, we explore this concept further by using
group-aided training within the distributional reinforcement learning paradigm.
Specifically, we propose an extension to categorical reinforcement learning,
where distributional learning targets are implicitly based on the total
information gathered by an ensemble. We empirically show that this may lead to
much more robust initial learning, a stronger individual performance level, and
good efficiency on a per-sample basis.
