World Models
Understanding how AI systems build internal representations of their environment to predict, plan, and imagine.
What Are World Models?
World models are internal representations that AI systems build to understand and predict their environment. Like how humans create mental models to anticipate outcomes, AI world models enable systems to:
- Predict future states without direct interaction
- Plan sequences of actions by simulating outcomes
- Learn more efficiently by imagining experiences
- Generate novel scenarios and creative solutions
- Understand causal relationships in complex systems
Core Components
State Representation
The model's encoding of the current environment state, often as a compressed latent vector.
Transition Model
Predicts how states evolve over time given actions, capturing environment dynamics.
Reward Model
Estimates expected rewards for state-action pairs, enabling value-based planning.
Observation Model
Reconstructs observations from latent states, enabling imagination and generation.
Interactive World Model
Click on the canvas to apply force to the ball. The green trail shows the model's prediction.
Mathematical Framework
Probabilistic Formulation
World models often use probabilistic approaches to handle uncertainty:
Learning Objective
Models are trained to minimize prediction error:
Combining reconstruction loss, reward prediction, and regularization.
Continue Learning
Key Papers
- • World Models (Ha & Schmidhuber, 2018)
- • MuZero (Schrittwieser et al., 2020)
- • DreamerV3 (Hafner et al., 2023)
- • JEPA (LeCun, 2022)
Implementations
- • Dreamer (TensorFlow)
- • PlaNet (PyTorch)
- • MuZero (JAX)
- • WorldModelsExperiments
Next Topics
- • Model-Based RL
- • Planning Algorithms
- • Video Prediction
- • Causal Reasoning