Keywords: reinforcement learning, offline RL, RL dataset, procedural generation, human demonstrations
TL;DR: We introduce and evaluate a new large-scale dataset for the game of NetHack, including 10 billion transitions from humans, 3 billion from a symbolic bot, and code for researchers to record and load their own trajectories.
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 for learning from demonstrations, showing that significant research advances are needed to fully leverage large-scale datasets for challenging sequential decision making tasks.
Supplementary Material: pdf
Contribution Process Agreement: Yes
In Person Attendance: Yes
Dataset Url: Submission: https://github.com/dungeonsdatasubmission/dungeonsdata-neurips2022 Repo: https://github.com/facebookresearch/nle Dataset Document: https://github.com/facebookresearch/nle/blob/main/DATASET.md
License: The dataset and code are submitted under the NetHack General License which can be found here: https://github.com/facebookresearch/nle/blob/main/LICENSE
Author Statement: Yes