World Model

World models are computational representations of environments, aiming to predict future states based on actions, enabling more efficient and robust decision-making in artificial intelligence. Current research focuses on improving the accuracy and generalization of these models, particularly through the use of transformer-based architectures, generative models (like diffusion models and VAEs), and techniques like model-based reinforcement learning. This work is significant because accurate world models are crucial for developing autonomous agents capable of complex reasoning and planning in diverse, real-world scenarios, impacting fields like robotics, autonomous driving, and healthcare.