  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.
