TY - GEN
T1 - BEDD: The MineRL BASALT Evaluation and Demonstrations Dataset for Training and Benchmarking Agents that Solve Fuzzy Tasks
T2 - 37th Conference on Neural Information Processing Systems, NeurIPS 2023
AU - Milani, Stephanie
AU - Kanervisto, Anssi
AU - Ramanauskas, Karolis
AU - Schulhoff, Sander
AU - Houghton, Brandon
AU - Shah, Rohin
PY - 2023/12/16
Y1 - 2023/12/16
N2 - The MineRL BASALT competition has served to catalyze advances in learning from human feedback through four hard-to-specify tasks in Minecraft, such as create and photograph a waterfall. Given the completion of two years of BASALT competitions, we offer to the community a formalized benchmark through the BASALT Evaluation and Demonstrations Dataset (BEDD), which serves as a resource for algorithm development and performance assessment. BEDD consists of a collection of 26 million image-action pairs from nearly 14, 000 videos of human players completing the BASALT tasks in Minecraft. It also includes over 3, 000 dense pairwise human evaluations of human and algorithmic agents. These comparisons serve as a fixed, preliminary leaderboard for evaluating newly-developed algorithms. To enable this comparison, we present a streamlined codebase for benchmarking new algorithms against the leaderboard. In addition to presenting these datasets, we conduct a detailed analysis of the data from both datasets to guide algorithm development and evaluation. The released code and data are available at https://github.com/minerllabs/basalt-benchmark.
AB - The MineRL BASALT competition has served to catalyze advances in learning from human feedback through four hard-to-specify tasks in Minecraft, such as create and photograph a waterfall. Given the completion of two years of BASALT competitions, we offer to the community a formalized benchmark through the BASALT Evaluation and Demonstrations Dataset (BEDD), which serves as a resource for algorithm development and performance assessment. BEDD consists of a collection of 26 million image-action pairs from nearly 14, 000 videos of human players completing the BASALT tasks in Minecraft. It also includes over 3, 000 dense pairwise human evaluations of human and algorithmic agents. These comparisons serve as a fixed, preliminary leaderboard for evaluating newly-developed algorithms. To enable this comparison, we present a streamlined codebase for benchmarking new algorithms against the leaderboard. In addition to presenting these datasets, we conduct a detailed analysis of the data from both datasets to guide algorithm development and evaluation. The released code and data are available at https://github.com/minerllabs/basalt-benchmark.
UR - http://www.scopus.com/inward/record.url?scp=85191160136&partnerID=8YFLogxK
M3 - Chapter in a published conference proceeding
AN - SCOPUS:85191160136
VL - 36
T3 - Advances in Neural Information Processing Systems
BT - Advances in Neural Information Processing Systems 36 (NeurIPS 2023)
Y2 - 10 December 2023 through 16 December 2023
ER -