Contrastive World Models: dropping pixel reconstruction beats Dreamer in visually complex environments
burny_tech · x · 2026-09-27
A new paper argues that pixel-reconstruction world models waste capacity modeling irrelevant background noise. Building on DeepMind's Dreamer, the authors remove the pixel decoder and instead maximize mutual information between state-action sequences and local patch features of future observations, using a Deep InfoMax-style objective.
Key results:
- Matches Dreamer and a momentum-prediction baseline in clean environments
- Substantially outperforms both with moving distractors and natural-video backgrounds
- Trains more efficiently since the decoder is gone entirely
The authors suggest contrastive, infomax-based objectives are a principled path to world models robust to visual nuisance factors — key for transferring model-based RL agents to the real world.
Related event: Contrastive World Models Drop Pixel Reconstruction and Beat Dreamer(5 posts)→
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