Ask what should change under a concrete input, then trace that expectation through the equation.
Foundation Lab
World Models & Model-Based RL
Sample-efficient RL: learn from imagined experience, not just real data
#76World ModelsTheory
key equation
Selected Foundation Object
Keep the equation fixed; move through the evidence.
Concept 76 of 100World ModelsTheory / Phase 10: Mathematical foundations & information geometry
Current question
World models let agents "imagine" consequences without taking real actions
Use the key equation and canonical papers as the available witnesses, without implying that a runnable panel exists.
The useful learning product is the reusable mechanism you can carry into another model, paper, or engineering tradeoff.
Use prerequisites, dependents, and semantic links to repair the next gap without leaving the object behind.
Why It Matters for Modern Models
- Sample-efficient RL: learn from imagined experience, not just real data
- DreamerV3 achieves superhuman Atari with 100× less data than model-free methods
- Foundation for planning-based AI: simulate futures before acting
What Tutorials Skip
What is still poorly explained in textbooks and papers:
- World models let agents "imagine" consequences without taking real actions
- Latent space prediction is easier than pixel prediction—compress, then predict
- Model error compounds over long horizons—need careful uncertainty handling
Visualization Status
Core Math (Optional Deep Dive)
If you want intuition first, start with the key equation and cited sources. Come back here for the full walkthrough.
Key Equation
A world model learns to predict future states:
Latent world model (DreamerV3):
- Encoder:
- Dynamics:
- Decoder:
Planning in imagination:
Train policy entirely in the learned model ("dreaming").