Summary of Zero-shot Generalization Of Vision-based Rl Without Data Augmentation, by Sumeet Batra et al.
Zero-Shot Generalization of Vision-Based RL Without Data Augmentation
by Sumeet Batra, Gaurav S. Sukhatme
First submitted to arxiv on: 9 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 A machine learning paper proposes Associative Latent DisentAnglement (ALDA), a model that enables zero-shot generalization in vision-based reinforcement learning (RL) agents. The authors draw inspiration from computational neuroscience, revisiting the role of latent disentanglement in RL and combining it with associative memory. They show that ALDA achieves zero-shot generalization on task variations without data augmentation. This approach has implications for understanding data augmentation techniques as a form of weak disentanglement. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way to help robots learn from experience is being developed. Right now, training these “reinforcement learning” robots takes a lot of time and data. Scientists are trying to make it easier by collecting lots of data or using tricks to pretend the data is more varied than it really is. However, this makes it harder and more expensive to train the robots. In this study, researchers took ideas from how our brains work and created a new model called ALDA that can help robots learn faster and better. This means they don’t need as much data or special tricks to pretend the data is different. |
Keywords
» Artificial intelligence » Data augmentation » Generalization » Machine learning » Reinforcement learning » Zero shot
