Data augmentation can improve the generalization performance of imitation learning agents in game environments.
This survey provides a comprehensive overview of the conceptual architecture, methodologies, and future research directions for large language model-based game agents (LLMGAs) across diverse game genres, including adventure, communication, competition, cooperation, simulation, and crafting & exploration.
This paper presents an innovative framework that integrates Large Language Models (LLMs) with an external Thinker module to enhance the reasoning capabilities of LLM-based agents. The framework forms a reasoning hierarchy where LLMs handle intuitive System-1 tasks, while the Thinker focuses on cognitive System-2 tasks that require complex logical analysis and domain-specific knowledge.