Text Based Game

Text-based games serve as a valuable testing ground for artificial intelligence, particularly in natural language processing and reinforcement learning, aiming to create agents capable of understanding and interacting with complex textual environments. Current research focuses on improving agent performance through techniques like incorporating Theory of Mind, utilizing large language models (LLMs) for action selection and world simulation, and employing reinforcement learning algorithms such as actor-critic methods and maximum entropy approaches. These advancements contribute to a deeper understanding of AI capabilities in handling complex decision-making under uncertainty and have implications for various applications, including game development, automated content creation, and the development of more robust and adaptable AI systems.