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arxiv: 2303.13512 · v1 · pith:T7QLPXA2 · submitted 2023-03-23 · cs.AI

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 Galbraith , Ke Chen
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Yan Song Tianze Zhou Bingquan Yu He Liu Kai Guan Yujing Hu Tangjie Lv Federico Malato Florian Leopold Amogh Raut Ville Hautam\"aki Andrew Melnik Shu Ishida Jo\~ao F. Henriques Robert Klassert Walter Laurito Ellen Novoseller Vinicius G. Goecks Nicholas Waytowich David Watkins Josh Miller Rohin Shah
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classification cs.AI
keywords competitionfeedbackhumanbasaltalgorithmsfine-tuningminerltasks
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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.

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  1. Ghost in the Minecraft: Generally Capable Agents for Open-World Environments via Large Language Models with Text-based Knowledge and Memory

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    GITM uses LLMs to generate action plans from text knowledge and memory, enabling agents to complete long-horizon Minecraft tasks at much higher success rates than prior RL methods.