Enhancing Macro-Management Research in StarCraft II: Dataset Improvements and Advanced AI Methods
DOI:
https://doi.org/10.66069/ojspub.26820902Keywords:
StarCraft II, Real-Time Strategy Games, Macro-Management, MSC Dataset, Deep Learning, Attention Mechanisms, Graph Neural Networks, Reinforcement Learning, Data Imbalance, AI AgentsAbstract
Macro-management constitutes a critical dimension of real-time strategy (RTS) games such as StarCraft II, encompassing high-level decision-making processes including resource allocation, unit production, and technology development. The MSC dataset, as introduced in the original study, provided an initial platform for investigating macro-management tasks using deep learning models. However, both the dataset and existing baseline models exhibit limitations that warrant further improvement. This paper examines the challenges and opportunities associated with advancing macro-management research in StarCraft II. We propose enhancements to the dataset by incorporating new features, addressing data imbalance, and updating preprocessing techniques. Furthermore, we review recent research and state-of-the-art methods in RTS games, such as attention mechanisms, graph neural networks, and reinforcement learning, which can be applied to improve existing tasks or introduce new research directions. We also present experimental results that highlight the effectiveness of the proposed improvements and novel approaches. Our objective is to inspire the research community to explore advanced AI techniques and strategies in macro-management and to contribute to the development of more capable AI agents in complex RTS games.
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