  Large language models (LLMs) have recently garnered significant
accomplishments in various exploratory tasks, even surpassing the performance
of traditional reinforcement learning-based methods that have historically
dominated the agent-based field. The purpose of this paper is to investigate
the efficacy of LLMs in executing real-time strategy war tasks within the
StarCraft II gaming environment. In this paper, we introduce SwarmBrain, an
embodied agent leveraging LLM for real-time strategy implementation in the
StarCraft II game environment. The SwarmBrain comprises two key components: 1)
a Overmind Intelligence Matrix, powered by state-of-the-art LLMs, is designed
to orchestrate macro-level strategies from a high-level perspective. This
matrix emulates the overarching consciousness of the Zerg intelligence brain,
synthesizing strategic foresight with the aim of allocating resources,
directing expansion, and coordinating multi-pronged assaults. 2) a Swarm
ReflexNet, which is agile counterpart to the calculated deliberation of the
Overmind Intelligence Matrix. Due to the inherent latency in LLM reasoning, the
Swarm ReflexNet employs a condition-response state machine framework, enabling
expedited tactical responses for fundamental Zerg unit maneuvers. In the
experimental setup, SwarmBrain is in control of the Zerg race in confrontation
with an Computer-controlled Terran adversary. Experimental results show the
capacity of SwarmBrain to conduct economic augmentation, territorial expansion,
and tactical formulation, and it shows the SwarmBrain is capable of achieving
victory against Computer players set at different difficulty levels.
