SwarmBrain: Embodied agent for real-time strategy game StarCraft II via large language models
Xiao Shao, Weifu Jiang, Fei Zuo, Mengqing Liu

TL;DR
This paper presents SwarmBrain, an embodied agent using large language models to perform real-time strategy tasks in StarCraft II, demonstrating effective macro and micro management leading to victories against computer opponents.
Contribution
Introduces SwarmBrain, combining LLM-powered strategic planning with a fast reflex mechanism for real-time decision-making in StarCraft II.
Findings
SwarmBrain effectively manages economy and expansion.
It achieves victories against various difficulty levels.
The system demonstrates real-time tactical responses.
Abstract
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,…
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Taxonomy
TopicsArtificial Intelligence in Games · Human Motion and Animation · Topic Modeling
MethodsSparse Evolutionary Training
