5 Best Resources to Learn About World Models in 2026

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. UC Berkeley's CS 285: Deep Reinforcement Learning, taught by Sergey Levine, covers the foundations required to understand why world models are useful in the first place. The course covers reinforcement learning, control, and deep learning, and also includes dedicated material on model-based reinforcement learning. The most relevant parts are the lectures on planning, optimal control, learned dynamics, and model-based reinforcement learning. It has lecture recordings, notes, and assignments, so you can study both the mathematics and practical implementations.

2. Stanford CS348K: Introduction to World Models

3. World Models by David Ha and Jurgen Schmidhuber

1. The vision model compresses observations into a smaller latent representation
2. The memory model learns how those latent states change over time
3. The controller uses this learned representation to decide which action to take