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

Max Schwarzer, Johan Samir Obando Ceron, Aaron Courville, Marc Bellemare, Rishabh Agarwal, Pablo Samuel Castro

Bigger, Better, Faster: Human-level Atari with human-level efficiency: 5 upvotes on Hugging Face Daily Papers, #2 of 11 papers on 2023-06-01. Day-by-day upvote history.

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 page on Hugging Face · arXiv

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