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Abstract
Research on StarCraft II (SC2) is considered important due to its similarity to real-life tasks and its potential to inspire game AI design. However, the complexity of SC2 presents considerable challenges. In 2019, DeepMind proposed AlphaStar (AS), an agent that achieved Grandmaster level in SC2. Nevertheless, the reasons for AS's success remain unclear. In this paper, we revisit AS by analyzing its technical details, implementation codes, and replays. We also propose the open-sourced mini-scaled AS's new versions to do ablation studies. We classify SC2 problems by difficulty level and suggest a research path for tackling them. We then identify several limitations of AS, such as its lack of strategic view, reasoning, scouting, changes in tactics, and planning. Our paper also presents the first analysis of AS's replays. In conclusion, we emphasize that there is still a long way to solve the final SC2 problem.
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