Bigger, Better, Faster: Human-level Atari with human-level efficiency
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.
64781840950ad3b14dde3641
Max Schwarzer
64781840950ad3b14dde3642
Johan Obando-Ceron
claimed_verified
2023-06-02T07:16:41.578Z
6478f30ea68454566353ef95
https://cdn-avatars.huggingface.co/v1/production/uploads/6478f30ea68454566353ef95/hX5NiVPyQbK8TIBKnT4J1.jpeg
Johan Samir Obando Ceron
user
johanobandoc
64781840950ad3b14dde3643
Aaron Courville
64781840950ad3b14dde3644
Marc Bellemare
64781840950ad3b14dde3645
Rishabh Agarwal
claimed_verified
2023-06-01T16:29:08.144Z
647875e37b370854241b89a8
/avatars/fd25f7e5960e4e132b9ee689f114820b.svg
Rishabh Agarwal
user
agarwl
64781840950ad3b14dde3646
Pablo Samuel Castro
admin_assigned
2023-06-01T07:33:00.348Z
6303fabc7373aacccd87ddcf
https://cdn-avatars.huggingface.co/v1/production/uploads/1661205153867-noauth.png
Pablo Samuel Castro
user
pcastr
Bigger, Better, Faster: Human-level Atari with human-level efficiency
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.
The BBF RL agent achieves super-human performance in Atari 100K by scaling neural networks for value estimation with sample efficiency.
value-based RL neural networks value estimation sample-efficient Atari 100K ALE