Summary of Unsupervised Salient Patch Selection For Data-efficient Reinforcement Learning, by Zhaohui Jiang et al.
Unsupervised Salient Patch Selection for Data-Efficient Reinforcement Learning
by Zhaohui Jiang, Paul Weng
First submitted to arxiv on: 10 Jan 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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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 A novel deep reinforcement learning (DRL) method, called SPIRL, is proposed to improve the sample efficiency of vision-based DRL. The approach relies on pre-trained Vision Transformer models that learn to reconstruct images from randomly-sampled patches, enabling the detection and selection of salient patches. These patches are then processed by an attention module within the RL agent. Experimental results demonstrate the data-efficiency of SPIRL on Atari games, outperforming state-of-the-art methods including traditional model-based approaches and keypoint-based models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way to make deep reinforcement learning (DRL) work better is proposed. It’s called SPIRL, and it uses special computer vision models that can learn from incomplete images. These models are trained to fill in missing pieces of an image, which helps them figure out what the important parts of the image are. The important parts are then used by a DRL agent to make decisions. Tests show that this approach is more efficient than other ways of doing DRL and works well on games like Atari. |
Keywords
» Artificial intelligence » Attention » Reinforcement learning » Vision transformer
