Summary of A Recipe For Unbounded Data Augmentation in Visual Reinforcement Learning, by Abdulaziz Almuzairee et al.
A Recipe for Unbounded Data Augmentation in Visual Reinforcement Learning
by Abdulaziz Almuzairee, Nicklas Hansen, Henrik I. Christensen
First submitted to arxiv on: 27 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computer Vision and Pattern Recognition (cs.CV); Robotics (cs.RO)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper proposes a novel recipe for data augmentation, SADA, to improve the training stability and visual generalization of Q-learning algorithms. Building upon prior work, SVEA, which uses selective data augmentation to enhance visual generalization without destabilizing training, the authors find that SVEA’s effectiveness is limited to photometric augmentations. To address this limitation, they introduce SADA, a generalized recipe that works with various types of augmentations. The proposed method is benchmarked on the DMControl Generalization Benchmark, Meta-World, and the Distracting Control Suite, demonstrating improved training stability and generalization. The authors’ contribution is significant, as SADA can be applied to a wide range of augmentations, making it a valuable tool for real-world applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about improving artificial intelligence (AI) that learns from what it sees. Right now, AI algorithms have trouble learning from pictures because they get too good at recognizing things in the training data and can’t generalize to new situations. The authors of this paper want to fix this by proposing a new way to add variety to the training data, called SADA. They test their method on several different tasks and show that it makes AI more stable and able to learn from a wider range of images. |
Keywords
* Artificial intelligence * Data augmentation * Generalization
