  The Arcade Learning Environment (ALE) has become an essential benchmark for
assessing the performance of reinforcement learning algorithms. However, the
computational cost of generating results on the entire 57-game dataset limits
ALE's use and makes the reproducibility of many results infeasible. We propose
a novel solution to this problem in the form of a principled methodology for
selecting small but representative subsets of environments within a benchmark
suite. We applied our method to identify a subset of five ALE games, called
Atari-5, which produces 57-game median score estimates within 10% of their true
values. Extending the subset to 10-games recovers 80% of the variance for
log-scores for all games within the 57-game set. We show this level of
compression is possible due to a high degree of correlation between many of the
games in ALE.
