优化星际争霸II数据存储，降低创建与使用成本。
Carefully Structured Compression: Efficiently Managing StarCraft II Data
- 设计专用序列化框架，提升复杂游戏数据管理效率。
- 相比AlphaStar-Unplugged，创建与存储成本显著降低。
- 支持深度学习训练，适合数字孪生等相似场景使用。
机器学习中的数据集创建与存储常被忽视，尤其对于星际争霸II这类结构复杂的实时策略游戏数据，需更精心的设计以降低拥有成本。本文提出一种针对星际争霸II的序列化框架，有效减少数据集的创建与存储开销，并改善使用体验。在与现有最可比数据集AlphaStar-Unplugged的对比中，验证了本框架在创建与存储成本上的优势。利用该数据集训练的深度学习模型性能优于基于其他数据集训练的模型。所提出的转换与使用框架已开源，适用于具有类似特征的数据集（如数字孪生仿真）。预转换的星际争霸II赛事数据亦可在线获取。
原文摘要 · Abstract (English)
Creation and storage of datasets are often overlooked input costs in machine learning, as many datasets are simple image label pairs or plain text. However, datasets with more complex structures, such as those from the real time strategy game StarCraft II, require more deliberate thought and strategy to reduce cost of ownership. We introduce a serialization framework for StarCraft II that reduces the cost of dataset creation and storage, as well as improving usage ergonomics. We benchmark against the most comparable existing dataset from \textit{AlphaStar-Unplugged} and highlight the benefit of our framework in terms of both the cost of creation and storage. We use our dataset to train deep learning models that exceed the performance of comparable models trained on other datasets. The dataset conversion and usage framework introduced is open source and can be used as a framework for datasets with similar characteristics such as digital twin simulations. Pre-converted StarCraft II tournament data is also available online.
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