StarCraft II (SC2) is a real-time strategy game, in which players produce and control multiple units to win. Due to its difficulties, such as huge state space, various action space, a long time horizon, and imperfect information, SC2 has been a research highlight in reinforcement learning research. Recently, an SC2 agent called AlphaStar is proposed which shows excellent performance, obtaining a high win-rates of 99.8% against Grandmaster level human players. We implemented a mini-scaled version of it called mini-AlphaStar based on their paper and the pseudocode they provided. The usage and analysis of it are shown in this technical report. The difference between AlphaStar and mini-AlphaStar is that we substituted the hyper-parameters in the former version with much smaller ones for mini-scale training. The codes of mini-AlphaStar are all open-sourced. The objective of mini-AlphaStar is to provide a reproduction of the original AlphaStar and facilitate the future research of RL on large-scale problems.
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