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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) |
Main editorial body preserved directly in static HTML.
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 |
|---|---|
| Understanding the RSSM Architecture | 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.
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