Is Deep Reinforcement Learning Really Superhuman on Atari? Leveling the
  playing field
  Consistent and reproducible evaluation of Deep Reinforcement Learning (DRL)
is not straightforward. In the Arcade Learning Environment (ALE), small changes
in environment parameters such as stochasticity or the maximum allowed play
time can lead to very different performance. In this work, we discuss the
difficulties of comparing different agents trained on ALE. In order to take a
step further towards reproducible and comparable DRL, we introduce SABER, a
Standardized Atari BEnchmark for general Reinforcement learning algorithms. Our
methodology extends previous recommendations and contains a complete set of
environment parameters as well as train and test procedures. We then use SABER
to evaluate the current state of the art, Rainbow. Furthermore, we introduce a
human world records baseline, and argue that previous claims of expert or
superhuman performance of DRL might not be accurate. Finally, we propose
Rainbow-IQN by extending Rainbow with Implicit Quantile Networks (IQN) leading
to new state-of-the-art performance. Source code is available for
reproducibility.
