R2-Dreamer: Redundancy-Reduced World Models without Decoders or Augmentation
Abstract
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 Twins, which can be easily integrated into existing frameworks. On DeepMind Control Suite and Meta-World, R2-Dreamer is competitive with strong baselines such as DreamerV3 and TD-MPC2 while training 1.59x faster than DreamerV3, and yields substantial gains on DMC-Subtle with tiny task-relevant objects. These results suggest that an effective internal regularizer can enable versatile, high-performance decoder-free MBRL. Code is available at https://github.com/NM512/r2dreamer.
Cite
@article{arxiv.2603.18202,
title = {R2-Dreamer: Redundancy-Reduced World Models without Decoders or Augmentation},
author = {Naoki Morihira and Amal Nahar and Kartik Bharadwaj and Yasuhiro Kato and Akinobu Hayashi and Tatsuya Harada},
journal= {arXiv preprint arXiv:2603.18202},
year = {2026}
}
Comments
20 pages, 12 figures, 2 tables