Summary of Sparsity-based Safety Conservatism For Constrained Offline Reinforcement Learning, by Minjae Cho et al.
Sparsity-based Safety Conservatism for Constrained Offline Reinforcement Learning
by Minjae Cho, Chuangchuang Sun
First submitted to arxiv on: 17 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 work in Offline Reinforcement Learning (RL) aims to address the challenges of extrapolation and interpolation errors in decision-making fields like autonomous driving and robotic manipulation. By introducing additional constraints, earlier studies have attempted to confine policy behavior towards more cautious decision-making. However, these methods may not effectively tackle interpolation errors, which can lead to significant safety breaches due to estimation errors. To mitigate this risk, the authors propose conservative metrics based on data sparsity that demonstrate high generalizability and efficacy compared to using bi-level cost-ub-maximization. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Offline RL is an alternative approach to traditional RL methods, which rely on real-time feedback. This approach is particularly useful in costly or hazardous settings where conducting additional experiments is impractical, and abundant datasets are available. However, the challenge of distributional shift poses a risk in offline RL, potentially leading to significant safety breaches due to estimation errors. To address this issue, the authors propose conservative metrics based on data sparsity that can help identify areas where hazards may be more prevalent than initially estimated. |
Keywords
* Artificial intelligence * Reinforcement learning
