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.

翻译：大型语言模型（LLMs）近期在各种探索性任务中取得了显著成就，甚至超越了传统基于强化学习的方法——后者历来主导着智能体领域。本文旨在研究LLMs在星际争霸II游戏环境中执行即时战略战争任务的有效性。我们提出SwarmBrain，一种利用LLM在星际争霸II游戏环境中实现即时战略的具身智能体。SwarmBrain包含两个关键组件：1）超脑智能矩阵，由最先进的LLMs驱动，旨在从高层视角统筹宏观策略。该矩阵模拟虫族智能大脑的整体意识，融合战略远见以分配资源、指导扩张并协调多线进攻。2）虫群反射网络，作为超脑智能矩阵深思熟虑决策的敏捷补充。鉴于LLM推理固有的延迟，虫群反射网络采用条件响应状态机框架，能够为虫族基础单位机动提供快速战术响应。实验设置中，SwarmBrain控制虫族与计算机控制的泰伦人族对抗。实验结果表明，SwarmBrain具备经济增强、领土扩张和战术制定的能力，并能战胜不同难度级别的计算机对手。