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Towards Solving Fuzzy Tasks with Human Feedback: A Retrospective of the MineRL BASALT 2022 Competition (2303.13512v1)
Published 23 Mar 2023 in cs.AI
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.
- Stephanie Milani (23 papers)
- Anssi Kanervisto (32 papers)
- Karolis Ramanauskas (6 papers)
- Sander Schulhoff (6 papers)
- Brandon Houghton (13 papers)
- Sharada Mohanty (13 papers)
- Byron Galbraith (1 paper)
- Ke Chen (241 papers)
- Yan Song (91 papers)
- Tianze Zhou (5 papers)
- Bingquan Yu (1 paper)
- He Liu (57 papers)
- Kai Guan (3 papers)
- Yujing Hu (28 papers)
- Tangjie Lv (35 papers)
- Federico Malato (7 papers)
- Florian Leopold (4 papers)
- Amogh Raut (4 papers)
- Andrew Melnik (33 papers)
- Shu Ishida (9 papers)