R2-Dreamer: Redundancy-Reduced World Models without Decoders or Augmentation

ArXi:2603.18202v1 Announce Type: cross 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.