# 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  
**arXiv:** [2305.19452](https://arxiv.org/abs/2305.19452)  
**Date:** 2023-05-30  
**Summary page:** https://www.summarizepaper.com/fr/paper/2305.19452v3/

## 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 https://github.com/google-research/google-research/tree/master/bigger_better_faster.