Distributional Reinforcement Learning with Ensembles

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 <PRE_TAG>reinforcement learning</POST_TAG> paradigm. Specifically, we propose an extension to categorical <PRE_TAG>reinforcement learning</POST_TAG>, 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.