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Computer games, as fully controlled simulated environments, have been utilized in significant scientific studies demonstrating the application of Reinforcement Learning (RL).Gaming and esports are key areas influenced by the application of Artificial Intelligence (AI) and Machine Learning (ML) solutions at scale.Tooling simplifies scientific workloads and is essential for developing the gaming and esports research area.Our tools primarily deliver software for working with StarCraft 2, a well-known real-time strategy (RTS) game and one of the long-standing esports titles.In this work, we present "SC2Tools", a toolset containing multiple submodules responsible for working with, and producing larger StarCraft 2 datasets.We provide a modular structure of the implemented tooling, leaving room for future extensions where needed.Additionally, some of the tools are not StarCraft 2 exclusive and can be used with other types of data for dataset creation.We provide PyTorch and PyTorch Lightning application programming interface (API) for easy access to the data.Finally, our solution provides some foundational work toward normalizing experiment workflow in StarCraft 2
Published in: The Journal of Open Source Software
Volume 11, Issue 118, pp. 8889-8889
DOI: 10.21105/joss.08889