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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.
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DreamerV3 is the clear successor: it retains DreamerV2's discrete representation insight while solving the hyperparameter tuning problem that limited DreamerV2's generality. DreamerV2 remains important as the model that proved discrete latent spaces could reach human-level Atari, a breakthrough that DreamerV3 builds upon.
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Choose DreamerV2 when its capabilities best match your research or deployment requirements.
Choose DreamerV3 when its capabilities best match your research or deployment requirements.
DreamerV3 is the clear successor: it retains DreamerV2's discrete representation insight while solving the hyperparameter tuning problem that limited DreamerV2's generality. DreamerV2 remains important as the model that proved discrete latent spaces could reach human-level Atari, a breakthrough that DreamerV3 builds upon.
| Dimension | DreamerV2 | DreamerV3 |
|---|---|---|
| Key Innovation | Discrete categorical latent space | Symlog predictions + fixed hyperparameters |
| Hyperparameters | Per-domain tuning required | Single set across all domains |
| Atari Performance | Human-level (first model-based) | Superhuman on 50+ games |
| Open-World | Not evaluated | First to collect diamonds in Minecraft |
| Objective | KL balancing | KL balancing + symlog normalization |
| Year | 2021 | 2023 |
High-level scoring context for the models referenced in this comparison.
| Model | Category | Index v1.1 | Confidence |
|---|---|---|---|
| DreamerV2 | Model-Based RL | 72/100 | high |
| DreamerV3 | Model-Based RL | 88/100 | high |
| PlaNet | Model-Based RL | 57/100 | high |
| RSSM | Latent Dynamics | 64/100 | medium |
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DreamerV3 in almost all cases. Its fixed hyperparameters mean you don't need domain-specific tuning, and it matches or exceeds DreamerV2 on every benchmark.
The core RSSM is similar, but DreamerV3 adds symlog predictions for scale-invariant learning, making it robust across environments with vastly different reward scales.
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Lead editor Bernard Grenat.
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