SCC: an efficient deep reinforcement learning agent mastering the game of StarCraft II
This work addresses the high computational cost bottleneck for researchers developing deep reinforcement learning agents for complex Real-Time Strategy games like StarCraft II.
This paper introduces StarCraft Commander (SCC), a deep reinforcement learning agent that achieves top human performance in StarCraft II, defeating GrandMaster players and professional players. It accomplishes this with significantly less computational resources compared to previous state-of-the-art agents like AlphaStar.
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.