Summary of Make the Pertinent Salient: Task-relevant Reconstruction For Visual Control with Distractions, by Kyungmin Kim et al.
Make the Pertinent Salient: Task-Relevant Reconstruction for Visual Control with Distractions
by Kyungmin Kim, JB Lanier, Pierre Baldi, Charless Fowlkes, Roy Fox
First submitted to arxiv on: 13 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); 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 method to improve Model-Based Reinforcement Learning (MBRL) in visual control tasks by reducing the complexity of representation learning. Building on the popular MBRL method DREAMER, the authors introduce Segmentation Dreamer (SD), an auxiliary task that helps agents learn generalizable perception in distracting environments. SD uses segmentation masks to reconstruct only task-relevant components of image observations, making it easier for agents to focus on relevant information and ignore distractions. This approach achieves significantly better sample efficiency and greater final performance than prior work in modified DeepMind Control suite (DMC) and Meta-World tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper makes a big improvement to how computers learn new skills by making them pay attention to what’s important in their environment. Right now, it’s hard for computers to learn if there are lots of distracting things around. The authors came up with a way to help computers focus on the most important parts and ignore distractions. This helps computers learn faster and do better at tasks that require recognizing patterns. |
Keywords
» Artificial intelligence » Attention » Reinforcement learning » Representation learning
