BEDD：用于训练和基准测试解决模糊任务智能体的 MineRL BASALT 评估与演示数据集
BEDD: The MineRL BASALT Evaluation and Demonstrations Dataset for Training and Benchmarking Agents that Solve Fuzzy Tasks
AI总结：
本文发布 BEDD 基准数据集，提供海量 Minecraft 人类演示、成对偏好评估及配套代码，以支持模糊任务智能体的训练、比较与评测。
摘要：
MineRL BASALT 竞赛通过 Minecraft 中四个难以明确规范的任务（例如建造并拍摄瀑布）推动了从人类反馈中学习的进展。鉴于两届 BASALT 竞赛已经结束，我们通过 BASALT Evaluation and Demonstrations Dataset（BEDD）向社区提供一个形式化基准，作为算法开发和性能评估的资源。BEDD 包含来自近 14,000 段人类玩家在 Minecraft 中完成 BASALT 任务视频的 2,600 万个图像-动作对。它还包括超过 3,000 条针对人类智能体和算法智能体的密集成对人类评估。这些比较构成一个固定的初步排行榜，用于评估新开发的算法。为实现这种比较，我们提出了一个精简代码库，用于将新算法与排行榜进行基准测试。除展示这些数据集外，我们还对来自两个数据集的数据进行详细分析，以指导算法开发和评估。发布的代码和数据可在 https://github.com/minerllabs/basalt-benchmark 获取。
英文摘要：
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 .
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
