  Spatial reasoning tasks in multi-agent environments such as event prediction,
agent type identification, or missing data imputation are important for
multiple applications (e.g., autonomous surveillance over sensor networks and
subtasks for reinforcement learning (RL)). StarCraft II game replays encode
intelligent (and adversarial) multi-agent behavior and could provide a testbed
for these tasks; however, extracting simple and standardized representations
for prototyping these tasks is laborious and hinders reproducibility. In
contrast, MNIST and CIFAR10, despite their extreme simplicity, have enabled
rapid prototyping and reproducibility of ML methods. Following the simplicity
of these datasets, we construct a benchmark spatial reasoning dataset based on
StarCraft II replays that exhibit complex multi-agent behaviors, while still
being as easy to use as MNIST and CIFAR10. Specifically, we carefully summarize
a window of 255 consecutive game states to create 3.6 million summary images
from 60,000 replays, including all relevant metadata such as game outcome and
player races. We develop three formats of decreasing complexity: Hyperspectral
images that include one channel for every unit type (similar to multispectral
geospatial images), RGB images that mimic CIFAR10, and grayscale images that
mimic MNIST. We show how this dataset can be used for prototyping spatial
reasoning methods. All datasets, code for extraction, and code for dataset
loading can be found at https://starcraftdata.davidinouye.com
