  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.
