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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.
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DreamerV3 is the clear successor. PlaNet remains important as the foundational architecture that introduced latent dynamics planning via the RSSM. Choose DreamerV3 for any new project; study PlaNet to understand the foundational concepts.
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Choose DreamerV3 when its capabilities best match your research or deployment requirements.
Choose PlaNet when its capabilities best match your research or deployment requirements.
DreamerV3 is the clear successor. PlaNet remains important as the foundational architecture that introduced latent dynamics planning via the RSSM. Choose DreamerV3 for any new project; study PlaNet to understand the foundational concepts.
| Dimension | DreamerV3 | PlaNet |
|---|---|---|
| Architecture | RSSM with discrete latents + symlog | RSSM with continuous latents |
| Planning Method | Imagination-based actor-critic | CEM model-predictive control |
| Hyperparameters | Fixed across all domains | Per-domain tuning required |
| Performance | Superhuman across domains | Competitive, limited domains |
| Year | 2023 | 2019 |
| Sample Efficiency | Very high | High |
| Action Spaces | Discrete + Continuous | Continuous only |
High-level scoring context for the models referenced in this comparison.
| Model | Category | Index v1.1 | Confidence |
|---|---|---|---|
| DreamerV3 | Model-Based RL | 88/100 | high |
| PlaNet | Model-Based RL | 57/100 | high |
| RSSM | Latent Dynamics | 64/100 | medium |
| DreamerV2 | Model-Based RL | 72/100 | high |
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For new projects, DreamerV3 is superior in virtually every dimension. PlaNet is valuable for understanding the foundations of latent dynamics world models.
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Lead editor Bernard Grenat.
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