World Models

World Models are a type of artificial intelligence architecture that combines reinforcement learning with generative models to simulate an environment. These models learn to understand and predict the dynamics of their surroundings by creating an internal representation of the world, which allows them to plan and make decisions effectively. The primary characteristics of World Models include the ability to generate realistic simulations of environments and the capability to learn from limited data. They are commonly used in robotics, autonomous systems, and video game AI, where understanding and interacting with complex environments is crucial.