  Ensemble and auxiliary tasks are both well known to improve the performance
of machine learning models when data is limited. However, the interaction
between these two methods is not well studied, particularly in the context of
deep reinforcement learning. In this paper, we study the effects of ensemble
and auxiliary tasks when combined with the deep Q-learning algorithm. We
perform a case study on ATARI games under limited data constraint. Moreover, we
derive a refined bias-variance-covariance decomposition to analyze the
different ways of learning ensembles and using auxiliary tasks, and use the
analysis to help provide some understanding of the case study. Our code is open
source and available at https://github.com/NUS-LID/RENAULT.
