  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.
