R2-Dreamer: Redundancy-Reduced World Models without Decoders or Augmentation
A central challenge in image-based Model-Based Reinforcement Learning (MBRL) is to learn representations that distill essential information from irrelevant visual details. While promising, reconstruction-based methods often waste capacity on large task-irrelevant regions. Decoder-free methods instead learn robust representations by leveraging Data Augmentation (DA), but reliance on such external regularizers limits versatility. We propose R2-Dreamer, a decoder-free MBRL framework with a self-supervised objective that serves as an internal regularizer, preventing representation collapse without resorting to DA. The core of our method is a redundancy-reduction objective inspired by Barlow T...
| Authors | Naoki Morihira, Amal Nahar, Kartik Bharadwaj et al. |
|---|---|
| Primary category | cs.LG |
| Published | 18 March 2026 |
| Last updated at source | 20 March 2026 |
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