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| Attribute | Value |
|---|---|
| Guide | Understanding the RSSM Architecture |
| Summary | A technical guide to the Recurrent State-Space Model: the foundational architecture behind PlaNet and the Dreamer family of world models. |
| Related Models | 4 |
| Related Research | 1 |
| References | 3 |
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The Recurrent State-Space Model (RSSM) is the core architecture that powers PlaNet, DreamerV1, DreamerV2, and DreamerV3. It combines deterministic recurrent transitions with stochastic latent variables to model environment dynamics in a compressed latent space.
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The RSSM maintains two types of state: a deterministic component (computed by a GRU recurrent network) that captures predictable transitions, and a stochastic component (sampled from a learned distribution) that captures uncertainty and multi-modal outcomes. Together, they create a rich representation of world dynamics.
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The RSSM is trained using variational inference. The training loss combines reconstruction loss (can the model reconstruct observations from latent states?), KL divergence (is the prior consistent with the posterior?), and optional reward prediction loss. DreamerV3 adds symlog predictions for scale-invariant learning.
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The RSSM's hybrid design is elegant: the deterministic path provides a stable backbone for long-horizon prediction, while the stochastic path handles the inherent uncertainty of real environments. This balance enables both accurate short-term prediction and robust long-term imagination.
| 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. |
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Pure recurrent models can't capture multi-modal uncertainty. The stochastic component allows the model to represent multiple possible futures, which is critical for planning in stochastic environments.
PlaNet introduced the RSSM with continuous Gaussian latents. DreamerV2 switched to discrete categorical latents. DreamerV3 added symlog predictions and KL-balanced objectives for domain-agnostic training.
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Published by world-models.io editorial board.
Lead editor Bernard Grenat.
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