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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| Attribute | Value |
|---|---|
| Model | RSSM |
| Lab / Organization | DeepMind |
| Category | Latent Dynamics |
| Subtype | Latent Dynamics Architecture |
| World Model Type | Core dynamics architecture |
| Primary Domain | World model backbone |
| Architecture | Hybrid deterministic-stochastic recurrent model with encoder/decoder |
| Modality | Visual + Proprioceptive |
| Training Method | Variational inference with reconstruction and KL objectives |
| Status | active |
| Year | 2019 |
| Performance Index | 64/100 (medium confidence, v1.1) |
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The RSSM models environment dynamics in latent space using both deterministic and stochastic components. The deterministic path captures predictable transitions via a recurrent network, while the stochastic path models uncertainty through latent variables. This design forms the backbone of PlaNet, DreamerV1, V2, and V3.
RSSM is a core dynamics architecture developed by Google in 2019 for world model backbone.
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| Signal | Value |
|---|---|
| Definition | RSSM is a core dynamics architecture developed by Google in 2019 for world model backbone. |
| Short Description | Recurrent State-Space Model: the foundational architecture behind PlaNet and the entire Dreamer family of world models. |
| Benchmark Rows | 0 |
| FAQ Entries | 1 |
| Related Models | 3 |
| Related Guides | 1 |
| Related Research Topics | 1 |
| Last Updated | 2026-02-05 |
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| Reference | Link |
|---|---|
| Hafner et al., 2019. Learning Latent Dynamics for Planning from Pixels. | Open source |
Side-by-side comparisons already connected to this model.
| Comparison | Matchup | Summary |
|---|---|---|
| DreamerV3 vs PlaNet | DreamerV3 vs PlaNet | DreamerV3 represents the evolution of PlaNet's core ideas. Both use the RSSM architecture, but DreamerV3 adds discrete representations, symlog predictions, and fixed hyperparameters to achieve state-of-the-art performance across diverse domains. |
| DreamerV2 vs PlaNet | DreamerV2 vs PlaNet | Both use the RSSM architecture for latent dynamics, but DreamerV2 introduced discrete representations that dramatically improved performance. PlaNet pioneered the approach; DreamerV2 perfected it for Atari-scale environments. |
| DreamerV2 vs DreamerV3 | DreamerV2 vs DreamerV3 | The Dreamer lineage's two most impactful iterations: DreamerV2 achieved human-level Atari with discrete representations, while DreamerV3 eliminated hyperparameter tuning entirely with symlog predictions. |
Crawler-readable guide links tied to this model.
| Guide | Summary |
|---|---|
| RSSM Architecture Explained: Dreamer World Model | A technical guide to the Recurrent State-Space Model: the foundational architecture behind PlaNet and the Dreamer family of world models. |
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| Topic | Summary |
|---|---|
| Self-Supervised World Models | How self-supervised world models learn environment dynamics without rewards, from JEPA and V-JEPA to predictive latent representations. |
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The deterministic path captures predictable dynamics, while the stochastic path models uncertainty and multi-modal outcomes. Together they create a more robust world model.
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Published by world-models.io editorial board.
Lead editor Tyler D. - Technical editor, methodology and benchmark analysis.
This model page synthesizes primary papers, official model pages, benchmark evidence, and related world-models.io context into a reference resource.
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: 2026-02-05.
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