Dreamer-CDP: Improving Reconstruction-free World Models Via Continuous Deterministic Representation Prediction
Model-based reinforcement learning (MBRL) agents operating in high-dimensional observation spaces, such as Dreamer, rely on learning abstract representations for effective planning and control. Existing approaches typically employ reconstruction-based objectives in the observation space, which can render representations sensitive to task-irrelevant details. Recent alternatives trade reconstruction for auxiliary action prediction heads or view augmentation strategies, but perform worse in the Crafter environment than reconstruction-based methods. We close this gap between Dreamer and reconstruction-free models by introducing a JEPA-style predictor defined on continuous, deterministic repre...
| Authors | Michael Hauri, Friedemann Zenke |
|---|---|
| Primary category | cs.LG |
| Published | 7 March 2026 |
| Last updated at source | 14 April 2026 |
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