{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/bigger-better-faster-human-level-atari-with","title":"Bigger, Better, Faster: Human-level Atari with human-level efficiency","arxiv_id":"2305.19452","date":"2023-05-30","proceeding":null,"authors":["Max Schwarzer","Johan Obando-Ceron","Aaron Courville","Marc Bellemare","Rishabh Agarwal","Pablo Samuel Castro"],"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.","url_abs":"https://arxiv.org/abs/2305.19452v3","url_pdf":"https://arxiv.org/pdf/2305.19452v3.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"bigger-better-faster-human-level-atari-with","repo_url":"https://github.com/google-research/google-research/tree/master/bigger_better_faster","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"jax","reach":null},{"paper_slug":"bigger-better-faster-human-level-atari-with","repo_url":"https://github.com/google-research/google-research","is_official":0,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"tf","reach":null},{"paper_slug":"bigger-better-faster-human-level-atari-with","repo_url":"https://github.com/NoSavedDATA/PyTorch-BBF-Bigger-Better-Faster-Atari-100k","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}}],"tasks":[{"task_slug":"atari-games-100k","task_name":"Atari Games 100k"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/2305.19452","atlas_url":"https://app.syntology.ai/?focus=2305.19452","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}