  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.
