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Spatial reasoning tasks in multi-agent environments such as event prediction,agent type identification, or missing data imputation are important formultiple applications (e.g., autonomous surveillance over sensor networks andsubtasks for reinforcement learning (RL)). StarCraft II game replays encodeintelligent (and adversarial) multi-agent behavior and could provide a testbedfor these tasks; however, extracting simple and standardized representationsfor prototyping these tasks is laborious and hinders reproducibility. Incontrast, MNIST and CIFAR10, despite their extreme simplicity, have enabledrapid prototyping and reproducibility of ML methods. Following the simplicityof these datasets, we construct a benchmark spatial reasoning dataset based onStarCraft II replays that exhibit complex multi-agent behaviors, while stillbeing as easy to use as MNIST and CIFAR10. Specifically, we carefully summarizea window of 255 consecutive game states to create 3.6 million summary imagesfrom 60,000 replays, including all relevant metadata such as game outcome andplayer races. We develop three formats of decreasing complexity: Hyperspectralimages that include one channel for every unit type (similar to multispectralgeospatial images), RGB images that mimic CIFAR10, and grayscale images thatmimic MNIST. We show how this dataset can be used for prototyping spatialreasoning methods. All datasets, code for extraction, and code for datasetloading can be found at https://starcraftdata.davidinouye.com

StarCraftImage: A Dataset For Prototyping Spatial Reasoning Methods For Multi-Agent Environments