title: Bigger, Better, Faster: Human-level Atari with human-level efficiency
summary: 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.
paper_authors._id: 64781840950ad3b14dde3641
paper_authors.name: Max Schwarzer
paper_authors._id: 64781840950ad3b14dde3642
paper_authors.name: Johan Obando-Ceron
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2023-06-02T07:16:41.578Z
paper_authors._id: 6478f30ea68454566353ef95
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/6478f30ea68454566353ef95/hX5NiVPyQbK8TIBKnT4J1.jpeg
paper_authors.fullname: Johan Samir Obando Ceron
paper_authors.type: user
paper_authors.user: johanobandoc
paper_authors._id: 64781840950ad3b14dde3643
paper_authors.name: Aaron Courville
paper_authors._id: 64781840950ad3b14dde3644
paper_authors.name: Marc Bellemare
paper_authors._id: 64781840950ad3b14dde3645
paper_authors.name: Rishabh Agarwal
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2023-06-01T16:29:08.144Z
paper_authors._id: 647875e37b370854241b89a8
paper_authors.avatarUrl: /avatars/fd25f7e5960e4e132b9ee689f114820b.svg
paper_authors.fullname: Rishabh Agarwal
paper_authors.type: user
paper_authors.user: agarwl
paper_authors._id: 64781840950ad3b14dde3646
paper_authors.name: Pablo Samuel Castro
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-06-01T07:33:00.348Z
paper_authors._id: 6303fabc7373aacccd87ddcf
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/1661205153867-noauth.png
paper_authors.fullname: Pablo Samuel Castro
paper_authors.type: user
paper_authors.user: pcastr
paper_title: Bigger, Better, Faster: Human-level Atari with human-level efficiency
paper_summary: 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.
paper_ai_keywords: value-based RL agent BBF super-human performance Atari 100K benchmark neural networks value estimation sample-efficient design choices sample-efficient RL research ALE