  AlphaStar, the AI that reaches GrandMaster level in StarCraft II, is a
remarkable milestone demonstrating what deep reinforcement learning can achieve
in complex Real-Time Strategy (RTS) games. However, the complexities of the
game, algorithms and systems, and especially the tremendous amount of
computation needed are big obstacles for the community to conduct further
research in this direction. We propose a deep reinforcement learning agent,
StarCraft Commander (SCC). With order of magnitude less computation, it
demonstrates top human performance defeating GrandMaster players in test
matches and top professional players in a live event. Moreover, it shows strong
robustness to various human strategies and discovers novel strategies unseen
from human plays. In this paper, we will share the key insights and
optimizations on efficient imitation learning and reinforcement learning for
StarCraft II full game.
