Bigger, Better, Faster: Human-level Atari with human-level efficiency
Authors: name: Max Schwarzer, name: Johan Obando-Ceron, name: Aaron Courville, name: Marc Bellemare, name: Rishabh Agarwal, name: Pablo Samuel Castro

We introduce a value-based RL agent, which we call BBF, that achieves
super-human performance in the Atari 100K benchmark. BBF relies on scaling the
neural networks used for value estimation, as well as a number of other design
choices that enable this scaling in a sample-efficient manner. We conduct
extensive analyses of these design choices and provide insights for future
work. We end with a discussion about updating the goalposts for
sample-efficient RL research on the ALE. We make our code and data publicly
available at
https://github.com/google-research/google-research/tree/master/bigger_better_faster.