A Survey on Large Language Model-Based Game Agents

The development of game agents holds a critical role in advancing towards Artificial General Intelligence. The progress of Large Language Models (LLMs) offers an unprecedented opportunity to evolve and empower game agents with human-like decision-making capabilities in complex computer game environments. This paper provides a comprehensive overview of LLM-based game agents from a holistic viewpoint. First, we introduce the conceptual architecture of LLM-based game agents, centered around three core functional components: memory, reasoning and in/output. Second, we survey existing representative LLM-based game agents documented in the literature with respect to methodologies and adaptation agility across six genres of games, including adventure, communication, competition, cooperation, simulation, and crafting & exploration games. Finally, we present an outlook of future research and development directions in this burgeoning field. A curated list of relevant papers is maintained and made accessible at:this https URL.

@article{hu2025_2404.02039,
  title={ A Survey on Large Language Model-Based Game Agents },
  author={ Sihao Hu and Tiansheng Huang and Gaowen Liu and Ramana Rao Kompella and Fatih Ilhan and Selim Furkan Tekin and Yichang Xu and Zachary Yahn and Ling Liu },
  journal={arXiv preprint arXiv:2404.02039},
  year={ 2025 }
}