Summary of Dataset Distillation For Offline Reinforcement Learning, by Jonathan Light et al.
Dataset Distillation for Offline Reinforcement Learning
by Jonathan Light, Yuanzhe Liu, Ziniu Hu
First submitted to arxiv on: 29 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- 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 The proposed method uses data distillation to train and distill a better dataset for offline reinforcement learning, allowing for the synthesis of a high-quality dataset that can be used to train a policy model. The approach is shown to achieve similar performance to models trained on full datasets or using percentile behavioral cloning. The method has implications for scenarios where it is difficult to obtain a quality dataset or train a policy in the actual environment given offline data. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Offline reinforcement learning often requires a high-quality dataset, but this can be challenging to obtain. To address this issue, researchers propose using data distillation to create a better dataset that can then be used to train a policy model. The method is shown to work well, achieving similar performance to models trained on full datasets or using other methods. This approach has the potential to make offline reinforcement learning more practical and effective. |
Keywords
* Artificial intelligence * Distillation * Reinforcement learning
