{"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/insights-from-the-neurips-2021-nethack","title":"Insights From the NeurIPS 2021 NetHack Challenge","arxiv_id":"2203.11889","date":"2022-03-22","proceeding":null,"authors":["Eric Hambro","Sharada Mohanty","Dmitrii Babaev","Minwoo Byeon","Dipam Chakraborty","Edward Grefenstette","Minqi Jiang","DaeJin Jo","Anssi Kanervisto","Jongmin Kim","Sungwoong Kim","Robert Kirk","Vitaly Kurin","Heinrich Küttler","Taehwon Kwon","Donghoon Lee","Vegard Mella","Nantas Nardelli","Ivan Nazarov","Nikita Ovsov","Jack Parker-Holder","Roberta Raileanu","Karolis Ramanauskas","Tim Rocktäschel","Danielle Rothermel","Mikayel Samvelyan","Dmitry Sorokin","Maciej Sypetkowski","Michał Sypetkowski"],"abstract":"In this report, we summarize the takeaways from the first NeurIPS 2021 NetHack Challenge. Participants were tasked with developing a program or agent that can win (i.e., 'ascend' in) the popular dungeon-crawler game of NetHack by interacting with the NetHack Learning Environment (NLE), a scalable, procedurally generated, and challenging Gym environment for reinforcement learning (RL). The challenge showcased community-driven progress in AI with many diverse approaches significantly beating the previously best results on NetHack. Furthermore, it served as a direct comparison between neural (e.g., deep RL) and symbolic AI, as well as hybrid systems, demonstrating that on NetHack symbolic bots currently outperform deep RL by a large margin. Lastly, no agent got close to winning the game, illustrating NetHack's suitability as a long-term benchmark for AI research.","url_abs":"https://arxiv.org/abs/2203.11889v1","url_pdf":"https://arxiv.org/pdf/2203.11889v1.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":"insights-from-the-neurips-2021-nethack","repo_url":"https://github.com/dllllb/neurips2021-nethack-raph","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"nethack","task_name":"NetHack"},{"task_slug":"reinforcement-learning-1","task_name":"Reinforcement Learning (RL)"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/2203.11889","atlas_url":"https://app.syntology.ai/?focus=2203.11889","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2203.11889"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-25T09:33:49+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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