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The learned transition function within a latent space that predicts how hidden states evolve over time in response to actions. Latent dynamics models avoid computationally expensive pixel-level predictions by operating in compressed representation space.
Latent Dynamics is a glossary concept in the architecture layer of the world models knowledge base.
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| Attribute | Value |
|---|---|
| Term | Latent Dynamics |
| Category | Architecture |
| Definition | The learned transition function within a latent space that predicts how hidden states evolve over time in response to actions. Latent dynamics models avoid computationally expensive pixel-level predictions by operating in compressed representation space. |
| Related Models | 4 |
| Related Research | 1 |
| Model | Lab | Category |
|---|---|---|
| DreamerV3 | Google DeepMind | Model-Based RL |
| PlaNet | Model-Based RL | |
| RSSM | Latent Dynamics | |
| World Models (Ha & Schmidhuber) | Google Brain / IDSIA | Model-Based RL |
| Topic | Summary |
|---|---|
| Model-Based Reinforcement Learning | What model-based reinforcement learning is, how world models enable imagination-based planning, and why Dreamer, MuZero, PlaNet, and TD-MPC2 matter. |
| Term | Category | Definition |
|---|---|---|
| Latent Space | Architecture | A compressed, abstract representation of data learned by a neural network. In world models, the latent space encodes environment states in a compact form that captures essential dynamics while discarding irrelevant details. Models like DreamerV3 and PlaNet operate entirely in latent space for efficient planning. |
| RSSM (Recurrent State-Space Model) | Architecture | A neural network architecture that combines deterministic recurrent states with stochastic latent variables to model environment dynamics. The RSSM forms the backbone of the Dreamer family of world models, enabling both accurate state tracking and exploration through uncertainty modeling. |
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Published by world-models.io editorial board.
Lead editor Bernard Grenat.
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