Towards Solving Fuzzy Tasks with Human Feedback: A Retrospective of the MineRL BASALT 2022 Competition
Stephanie Milani
Anssi Kanervisto
Karolis Ramanauskas
Sander Schulhoff
Brandon Houghton
Sharada Mohanty
Byron V. Galbraith
Ke Chen
Yan Song
Tianze Zhou
Bingquan Yu
He Liu
Kai Guan
Yujing Hu
Tangjie Lv
Federico Malato
Florian Leopold
Amogh Raut
Ville Hautamaki
Andrew Melnik
Shu Ishida
João F. Henriques
Robert Klassert
Walter Laurito
Ellen R. Novoseller
Vinicius G. Goecks
Nicholas R. Waytowich
David Watkins
J. Miller
Rohin Shah

Abstract
To facilitate research in the direction of fine-tuning foundation models from human feedback, we held the MineRL BASALT Competition on Fine-Tuning from Human Feedback at NeurIPS 2022. The BASALT challenge asks teams to compete to develop algorithms to solve tasks with hard-to-specify reward functions in Minecraft. Through this competition, we aimed to promote the development of algorithms that use human feedback as channels to learn the desired behavior. We describe the competition and provide an overview of the top solutions. We conclude by discussing the impact of the competition and future directions for improvement.
View on arXivComments on this paper