Summary of Integrating Domain Knowledge For Handling Limited Data in Offline Rl, by Briti Gangopadhyay et al.
Integrating Domain Knowledge for handling Limited Data in Offline RL
by Briti Gangopadhyay, Zhao Wang, Jia-Fong Yeh, Shingo Takamatsu
First submitted to arxiv on: 11 Jun 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 novel domain knowledge-based regularization technique adapts the initial domain knowledge to significantly improve performance in limited data with partially omitted states. Offline Reinforcement Learning (RL) algorithms that learn from static datasets struggle when confronted with rare or unseen observations, resulting in sub-optimal performance. By introducing a regularization term that mitigates erroneous actions for sparse samples and unobserved states covered by domain knowledge, the authors demonstrate an average performance increase of at least 27% compared to existing offline RL algorithms operating on limited data. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way is found to make Offline Reinforcement Learning better when it has limited data. Right now, these algorithms don’t do well with rare or unseen observations. To fix this, the authors create a special kind of regularization that helps make better decisions for situations that aren’t covered by what’s already known. This makes the algorithm perform much better, with an average increase in performance of at least 27% compared to other offline RL algorithms. |
Keywords
» Artificial intelligence » Regularization » Reinforcement learning
