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
Once you understand some reinforcement learning, Stanford’s Introduction to World Models lecture provides a much more direct entry into the topic. The lecture is part of Stanford’s Spring 2026 CS348K course and is available online with downloadable PDF slides. It covers visual and spatial computing workloads including generative AI-based world creation, latent world representations, non-pixel-based approaches, and embodied agentic AI. It’s not a full course, but it is a good starting point for understanding what world models actually are.
3. World Models by David Ha and Jurgen Schmidhuber
This is probably the single most important resource for world models. The original World Models work by David Ha and Jürgen Schmidhuber popularized a simple but powerful idea: an agent can learn a compressed representation of its environment, learn how that representation evolves, and then train a controller using its internally simulated environment. The architecture consists of three main components.
- The vision model compresses observations into a smaller latent representation
- The memory model learns how those latent states change over time
- The controller uses this learned representation to decide which action to take
The most interesting idea is that an agent can sometimes train inside its learned environment rather than interacting with the real environment for every training step. The project website is good for beginners — it includes explanations, diagrams, animations, experiments, and links to the implementation. Even though modern systems such as Dreamer and large video-based world models are more complex, the core intuition is very similar.
4. Stanford World Models and Model-Based Reinforcement Learning Reading List
Once you understand the basic idea behind world models, the next step is figuring out what to read. There are now hundreds of papers related to learned dynamics, model-based reinforcement learning, latent environments, generative simulators, and embodied world models.
This Stanford reading list provides a much more manageable starting point. It currently highlights ten important papers tracing the progression from early neural world models to modern approaches. The list includes World Models, PlaNet, Dreamer, DreamerV2, SimPLe, DayDreamer, Genie, and TD-MPC2. This sequence is useful because you can see how the field evolved.
The original World Models showed that an agent could learn a compact internal simulator. PlaNet explored planning directly inside learned latent dynamics. Dreamer shifted toward learning policies using imagined trajectories. Later systems extended similar concepts into robotics and large-scale generative environments.
Instead of randomly searching for papers on world models, you can simply work through this reading list one paper at a time.
5. Awesome World Model + Comprehensive World Models Survey
Researchers associated with Tsinghua University created the Awesome World Model repository alongside their survey, Understanding World or Predicting Future? A Comprehensive Survey of World Models. The survey was accepted by ACM Computing Surveys, while the repository continuously organizes papers and resources across different branches of world-model research. It covers areas such as:
- Model-based reinforcement learning
- Self-supervised learning
- Large language and multimodal models
- Video generation and interactive 3D environments
- Game intelligence and embodied intelligence
- Autonomous driving and urban intelligence
The accompanying survey is especially useful once you have learned the basics, because it explains how all these apparently different research directions fit under the broader world-model umbrella. I would not recommend reading the entire repository from top to bottom. Instead, bookmark it and use it as a reference whenever you want to explore a particular branch of the field.
Bonus: World Models Handbook
If you prefer learning from a book rather than jumping between lectures and research papers, Michael Brenndoerfer’s World Models Handbook is one of the most relevant resources available. It can be read online for free, and a PDF version is also available for purchase. The book is still being completed: the site currently lists 72 planned chapters, with 39 published. It starts with a small end-to-end world model before introducing foundations such as dynamical systems, partial observability, Bayesian filtering, system identification, uncertainty, physics, and causality. Later sections move into modern architectures, including recurrent state-space models, transformers, autoregressive token models, diffusion and flow models, predictive embeddings such as JEPA, and multimodal foundation models. It also covers planning approaches and major research lines — Dreamer, MuZero, TD-MPC, Genie, Cosmos, and vision-language-action models.
Wrapping Up
World models are not one particular neural-network architecture. They lie at the intersection of reinforcement learning, representation learning, planning, video generation, robotics, control, and embodied AI, which makes them difficult to learn by simply opening the newest paper.
A better strategy is to work backward from the fundamentals. First understand how an agent can learn and plan with a model of its environment. Then study the original neural world models architecture. After that, explore how newer research extends the same idea to latent representations, large-scale simulators, video prediction, robotics, and physical AI.
Kanwal Mehreen is a machine learning engineer and a technical writer with a profound passion for data science and the intersection of AI with medicine. She co-authored the ebook “Maximizing Productivity with ChatGPT”. As a Google Generation Scholar 2022 for APAC, she champions diversity and academic excellence. She’s also recognized as a Teradata Diversity in Tech Scholar, Mitacs Globalink Research Scholar, and Harvard WeCode Scholar. Kanwal is an ardent advocate for change, having founded FEMCodes to empower women in STEM fields.
