Insights From the NeurIPS 2021 NetHack Challenge
Eric Hambro
Sharada Mohanty
Dmitrii Babaev
Minwoo Byeon
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
Dipam Chakraborty
Minqi Jiang
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
DATE PUBLISHED 
VENUE
Challenge 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.