Summary of Closing the Gap Between Td Learning and Supervised Learning — a Generalisation Point Of View, by Raj Ghugare et al.
Closing the Gap between TD Learning and Supervised Learning – A Generalisation Point of View
by Raj Ghugare, Matthieu Geist, Glen Berseth, Benjamin Eysenbach
First submitted to arxiv on: 20 Jan 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 proposed research explores the connection between reinforcement learning (RL) and combinatorial generalization. Specifically, it investigates whether certain off-the-shelf supervised-learning (SL) algorithms can stitch together pieces of experience to solve a task never seen before during training. The study shows that SL-based RL methods lack this stitching property, which is crucial for achieving combinatorial generalization. However, the authors propose a simple remedy by introducing temporal data augmentation, enabling SL-based methods to successfully complete tasks not seen together during training. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research looks at how machine learning models can learn from new situations that combine things they’ve never seen before. Some models are really good at this, but others aren’t as good at it. The study found that some popular models don’t have this ability to generalize in this way, which is important for doing well on tasks like recognizing audio or video. To fix this problem, the researchers came up with a new technique called temporal data augmentation, which helps these models learn better and do well on new situations. |
Keywords
* Artificial intelligence * Data augmentation * Generalization * Machine learning * Reinforcement learning * Supervised
