  StarCraft II (SC2) is a real-time strategy game in which players produce and
control multiple units to fight against opponent's units. Due to its
difficulties, such as huge state space, various action space, a long time
horizon, and imperfect information, SC2 has been a research hotspot in
reinforcement learning. Recently, an agent called AlphaStar (AS) has been
proposed, which shows good performance, obtaining a high win rate of 99.8%
against human players. We implemented a mini-scaled version of it called
mini-AlphaStar (mAS) based on AS's paper and pseudocode. The difference between
AS and mAS is that we substituted the hyper-parameters of AS with smaller ones
for mini-scale training. Codes of mAS are all open-sourced
(https://github.com/liuruoze/mini-AlphaStar) for future research.
