Improving Generalization in Game AI Agents through Data Augmentation in Imitation Learning
Data augmentation can improve the generalization performance of imitation learning agents in game environments.
A Comprehensive Survey on Large Language Model-Based Game Agents: Architectures, Methodologies, and Future Directions
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.
Enhancing Reasoning Capabilities of Large Language Models through an External Thinker Module: A Case Study in the Game of Werewolf
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.
© 2024 by Linnk AI