World models are becoming one of the most interesting areas of modern AI research. Instead of simply mapping an input to an output, a world model learns how an environment behaves . It can predict what might happen next, simulate possible outcomes, and help an AI system plan its actions. Although recent systems have brought world models into video generation, robotics, embodied AI, and physical reasoning, many of their fundamental ideas come from reinforcement learning and model-based planning. There is no single resource that takes you from the fundamentals all the way to systems such as Dreamer , Genie , and modern world foundation models, but we do have enough resources to understand how it all works. Here are the five best resources I found for learning about world models in 2026.

1. UC Berkeley CS 285: Deep Reinforcement Learning

If you are completely new to world models, I would start here rather than jumping directly into recent world-model papers.…