A world model is an AI system that learns a predictive representation of how an environment changes over time. It can use observations, actions, or language to estimate future states and possible outcomes before an agent acts. World models support planning in model-based reinforcement learning, robotics, embodied AI, autonomous driving, and interactive simulation. This site catalogs these systems with model records, research papers, benchmark context, and source links. Start with the world models database, compare systems on the leaderboard, or read the Performance Index methodology.
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A symmetric logarithmic transformation used in DreamerV3 to handle reward scales that vary across different environments. Symlog compresses large values while preserving small values, enabling a single set of hyperparameters to work across diverse domains.
Symlog Predictions is a glossary concept in the techniques layer of the world models knowledge base.
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| Attribute | Value |
|---|---|
| Term | Symlog Predictions |
| Category | Techniques |
| Definition | A symmetric logarithmic transformation used in DreamerV3 to handle reward scales that vary across different environments. Symlog compresses large values while preserving small values, enabling a single set of hyperparameters to work across diverse domains. |
| Related Models | 1 |
| Related Research | 1 |
| Model | Lab | Category |
|---|---|---|
| DreamerV3 | Google DeepMind | 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 Dynamics | Architecture | 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. |
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Published by world-models.io editorial board.
Lead editor Bernard Grenat.
This glossary page publishes stable definitions linked to related models, research topics, and primary-source context.
Each editorial page is assembled from primary sources, normalized into extractable summaries, checked for factual drift, and reviewed before publication or major refreshes. Last reviewed: 2026-09-27.
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