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.
Papers
Learning and Understanding a Disentangled Feature Representation for Hidden Parameters in Reinforcement Learning
Christopher Reale, Rebecca Russell
The Effectiveness of World Models for Continual Reinforcement Learning
Samuel Kessler, Mateusz Ostaszewski, Michał Bortkiewicz, Mateusz Żarski, Maciej Wołczyk, Jack Parker-Holder, Stephen J. Roberts, Piotr Miłoś