Retrospective on the 2021 MineRL BASALT Competition on Learning from Human Feedback
Loading...
Links to Files
Permanent Link
Author/Creator ORCID
Date
2022-07
Type of Work
Department
Program
Citation of Original Publication
Shah, R., Wang, S.H., Wild, C., Milani, S., Kanervisto, A., Goecks, V.G., Waytowich, N., Watkins-Valls, D., Prakash, B., Mills, E., Garg, D., Fries, A., Souly, A., Chan, J.S., del Castillo, D. & Lieberum, T.. (2022). Retrospective on the 2021 MineRL BASALT Competition on Learning from Human Feedback. <i>Proceedings of the NeurIPS 2021 Competitions and Demonstrations Track</i>, in <i>Proceedings of Machine Learning Research</i> 176:259-272 Available from https://proceedings.mlr.press/v176/shah22a.html.
Rights
This work was written as part of one of the author's official duties as an Employee of the United States Government and is therefore a work of the United States Government. In accordance with 17 U.S.C. 105, no copyright protection is available for such works under U.S. Law.
Public Domain Mark 1.0
Public Domain Mark 1.0
Subjects
Abstract
We held the first-ever MineRL Benchmark for Agents that Solve Almost-Lifelike Tasks (MineRL BASALT) Competition at the Thirty-fifth Conference on Neural Information Processing Systems (NeurIPS 2021). The goal of the competition was to promote research towards agents that use learning from human feedback (LfHF) techniques to solve open-world tasks. Rather than mandating the use of LfHF techniques, we described four tasks in natural language to be accomplished in the video game Minecraft, and allowed participants to use any approach they wanted to build agents that could accomplish the tasks. Teams developed a diverse range of LfHF algorithms across a variety of possible human feedback types. The three winning teams implemented significantly different approaches while achieving similar performance. Interestingly, their approaches performed well on different tasks, validating our choice of tasks to include in the competition. While the outcomes validated the design of our competition, we did not get as many participants and submissions as our sister competition, MineRL Diamond. We speculate about the causes of this problem and suggest improvements for future iterations of the competition.