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.

Players of the science-fiction video game StarCraft II faced an unusual opponent this summer. An artificial intelligence (AI) known as AlphaStar -- which was built by Google's AI firm DeepMind -- achieved a grandmaster rating after it was unleashed on the game's European servers, placing within the top 0.15% of the region's 90,000 players. The result, published on 30 October in Nature1, shows that an AI can compete at the highest levels of StarCraft II, a massively popular online strategy game in which players compete in real time as one of three factions -- the human Terran forces or the aliens Protoss and Zerg -- battling against each other in a futuristic warzone. DeepMind, which previously built world-leading AIs that play chess and Go, targeted StarCraft II as its next benchmark in the quest for a general AI -- a machine capable of learning or understanding any task that humans can -- because of the game's strategic complexity and rapid pace. "I did not expect AI to essentially be superhuman in this domain so quickly, maybe not for another couple of years," says Jon Dodge, an AI researcher at Oregon State University in Corvallis.