# Carefully Structured Compression: Efficiently Managing StarCraft II Data

arXiv: https://arxiv.org/abs/2410.08659 · HTML: https://mlsanity.com/paper/2410.08659/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 28 references that are in the map.
- First posted on arXiv 11 Oct 2024.

## Builds on (most cited first, 11 of 11)

- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [StarCraft II: A New Challenge for Reinforcement Learning](https://mlsanity.com/paper/1708.04782/) (2017)
- [Particle Transformer for Jet Tagging](https://mlsanity.com/paper/2202.03772/) (2022)
- [Large Language Models and Games: A Survey and Roadmap](https://mlsanity.com/paper/2402.18659/) (2024)
- [TexFusion: Synthesizing 3D Textures with Text-Guided Image Diffusion Models](https://mlsanity.com/paper/2310.13772/) (2023)
- [Optimized U-Net for Brain Tumor Segmentation](https://mlsanity.com/paper/2110.03352/) (2021)
- [Imitation Learning with Concurrent Actions in 3D Games](https://mlsanity.com/paper/1803.05402/) (2018)
- [MSC: A Dataset for Macro-Management in StarCraft II](https://mlsanity.com/paper/1710.03131/) (2017)
- [Collection and Validation of Psychophysiological Data from Professional and Amateur Players: a Multimodal eSports Dataset](https://mlsanity.com/paper/2011.00958/) (2020)
- [SC2EGSet: StarCraft II Esport Replay and Game-state Dataset](https://mlsanity.com/paper/2207.03428/) (2022)
- [StarCraftImage: A Dataset For Prototyping Spatial Reasoning Methods For Multi-Agent Environments](https://mlsanity.com/paper/2401.04290/) (2024)

## Built on by (most cited first, 1 of 1)

- [Play Like Champions: Counterfactual Feedback Generation in Latent Space](https://mlsanity.com/paper/2607.00190/) (2026)

## Abstract (arXiv)

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 textitAlphaStar-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.