Model Based Agent

Model-based agents leverage learned internal models of their environment to make decisions, aiming to improve efficiency and adaptability compared to model-free approaches. Current research focuses on integrating large language models (LLMs) to enhance reasoning and communication capabilities, often within frameworks employing reinforcement learning and planning algorithms like Dyna-Q, Dreamer, and various value decomposition methods. This work is significant for advancing artificial general intelligence (AGI) and has practical implications across diverse fields, including air traffic control, software engineering, and robotics, by enabling more autonomous and adaptable systems.