[2305.19452] Bigger, Better, Faster: Human-level Atari with human-level efficiency
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Computer Science > Machine Learning

arXiv:2305.19452 (cs)
[Submitted on 30 May 2023 (v1), last revised 13 Nov 2023 (this version, v3)]

Title:Bigger, Better, Faster: Human-level Atari with human-level efficiency

Authors:Max Schwarzer, Johan Obando-Ceron, Aaron Courville, Marc Bellemare, Rishabh Agarwal, Pablo Samuel Castro
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Abstract: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 this https URL.
Comments: ICML 2023, revised version
Subjects: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
Cite as: arXiv:2305.19452 [cs.LG]
  (or arXiv:2305.19452v3 [cs.LG] for this version)
  https://doi.org/10.48550/arXiv.2305.19452

Submission history

From: Max Schwarzer [view email]
[v1] Tue, 30 May 2023 23:23:25 UTC (747 KB)
[v2] Fri, 9 Jun 2023 05:17:43 UTC (750 KB)
[v3] Mon, 13 Nov 2023 17:57:19 UTC (743 KB)
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