1. Insights From the NeurIPS 2021 NetHack Challenge
- Author
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Hambro, Eric, Mohanty, Sharada, Babaev, Dmitrii, Byeon, Minwoo, Chakraborty, Dipam, Grefenstette, Edward, Jiang, Minqi, Jo, Daejin, Kanervisto, Anssi, Kim, Jongmin, Kim, Sungwoong, Kirk, Robert, Kurin, Vitaly, Küttler, Heinrich, Kwon, Taehwon, Lee, Donghoon, Mella, Vegard, Nardelli, Nantas, Nazarov, Ivan, Ovsov, Nikita, Parker-Holder, Jack, Raileanu, Roberta, Ramanauskas, Karolis, Rocktäschel, Tim, Rothermel, Danielle, Samvelyan, Mikayel, Sorokin, Dmitry, Sypetkowski, Maciej, and Sypetkowski, Michał
- Subjects
Computer Science - Machine Learning ,Computer Science - Artificial Intelligence ,Computer Science - Neural and Evolutionary Computing ,Computer Science - Symbolic Computation ,Statistics - Machine Learning - 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., Comment: Under review at PMLR for the NeuRIPS 2021 Competition Workshop Track, 10 pages + 10 in appendices
- Published
- 2022