  The development of game agents holds a critical role in advancing towards
Artificial General Intelligence (AGI). The progress of LLMs and their
multimodal counterparts (MLLMs) 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 six essential
functional components: perception, memory, thinking, role-playing, action, and
learning. 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: https://github.com/git-disl/awesome-LLM-game-agent-papers.
