BEDD: The MineRL BASALT Evaluation and Demonstrations Dataset for Training and Benchmarking Agents that Solve Fuzzy Tasks

TL;DR

The paper introduces BEDD, a comprehensive dataset and benchmark for training and evaluating AI agents on complex, fuzzy tasks in Minecraft, facilitating progress in learning from human feedback.

Contribution

It provides a large-scale dataset, evaluation framework, and analysis tools for benchmarking agents on difficult Minecraft tasks, advancing research in human feedback learning.

Findings

26 million image-action pairs in the dataset

Over 3,000 human evaluations for benchmarking

A streamlined codebase for algorithm comparison

Abstract

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 .