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StarCraft II is a challenging benchmark for AI agents due to the necessity ofboth precise micro level operations and strategic macro awareness. Previousworks, such as Alphastar and SCC, achieve impressive performance on tacklingStarCraft II , however, still exhibit deficiencies in long term strategicplanning and strategy interpretability. Emerging large language model (LLM)agents, such as Voyage and MetaGPT, presents the immense potential in solvingintricate tasks. Motivated by this, we aim to validate the capabilities of LLMson StarCraft II, a highly complex RTS game.To conveniently take full advantageof LLMs` reasoning abilities, we first develop textual StratCraft IIenvironment, called TextStarCraft II, which LLM agent can interact. Secondly,we propose a Chain of Summarization method, including single framesummarization for processing raw observations and multi frame summarization foranalyzing game information, providing command recommendations, and generatingstrategic decisions. Our experiment consists of two parts: first, an evaluationby human experts, which includes assessing the LLMs`s mastery of StarCraft IIknowledge and the performance of LLM agents in the game; second, the in gameperformance of LLM agents, encompassing aspects like win rate and the impact ofChain of Summarization.Experiment results demonstrate that: 1. LLMs possess therelevant knowledge and complex planning abilities needed to address StarCraftII scenarios; 2. Human experts consider the performance of LLM agents to beclose to that of an average player who has played StarCraft II for eight years;3. LLM agents are capable of defeating the built in AI at the Harder(Lv5)difficulty level. We have open sourced the code and released demo videos of LLMagent playing StarCraft II.

Large Language Models Play StarCraft II: Benchmarks and A Chain of Summarization Approach