Insights From the NeurIPS 2021 NetHack Challenge

• URL: http://arxiv.org/abs/2203.11889v1
• Date: Tue, 22 Mar 2022 17:01:07 GMT
• Title: Insights From the NeurIPS 2021 NetHack Challenge
• 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 K\"uttler, Taehwon Kwon, Donghoon Lee, Vegard Mella, Nantas Nardelli, Ivan Nazarov, Nikita Ovsov, Jack Parker-Holder, Roberta Raileanu, Karolis Ramanauskas, Tim Rockt\"aschel, Danielle Rothermel, Mikayel Samvelyan, Dmitry Sorokin, Maciej Sypetkowski, Micha{\l} Sypetkowski
• Abstract summary: The first NeurIPS 2021 NetHack Challenge showcased community-driven progress in AI. It served as a direct comparison between neural (e.g., deep RL) and symbolic AI, as well as hybrid systems. No agent got close to winning the game, illustrating NetHack's suitability as a long-term benchmark for AI research.
• Score: 40.52602443114554
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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