Dungeons and Data: A Large-Scale NetHack Dataset
November 01, 2022 ยท Declared Dead ยท ๐ Neural Information Processing Systems
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Authors
Eric Hambro, Roberta Raileanu, Danielle Rothermel, Vegard Mella, Tim Rocktรคschel, Heinrich Kรผttler, Naila Murray
arXiv ID
2211.00539
Category
cs.LG: Machine Learning
Cross-listed
cs.AI
Citations
21
Venue
Neural Information Processing Systems
Last Checked
3 months ago
Abstract
Recent breakthroughs in the development of agents to solve challenging sequential decision making problems such as Go, StarCraft, or DOTA, have relied on both simulated environments and large-scale datasets. However, progress on this research has been hindered by the scarcity of open-sourced datasets and the prohibitive computational cost to work with them. Here we present the NetHack Learning Dataset (NLD), a large and highly-scalable dataset of trajectories from the popular game of NetHack, which is both extremely challenging for current methods and very fast to run. NLD consists of three parts: 10 billion state transitions from 1.5 million human trajectories collected on the NAO public NetHack server from 2009 to 2020; 3 billion state-action-score transitions from 100,000 trajectories collected from the symbolic bot winner of the NetHack Challenge 2021; and, accompanying code for users to record, load and stream any collection of such trajectories in a highly compressed form. We evaluate a wide range of existing algorithms including online and offline RL, as well as learning from demonstrations, showing that significant research advances are needed to fully leverage large-scale datasets for challenging sequential decision making tasks.
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