Summary of Confidence Aware Inverse Constrained Reinforcement Learning, by Sriram Ganapathi Subramanian et al.
Confidence Aware Inverse Constrained Reinforcement Learning
by Sriram Ganapathi Subramanian, Guiliang Liu, Mohammed Elmahgiubi, Kasra Rezaee, Pascal Poupart
First submitted to arxiv on: 24 Jun 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 Inverse Constraint Reinforcement Learning (ICRL) method allows users to specify a desired level of confidence in the estimated constraints, ensuring they only use those that meet the required threshold. By taking expert demonstrations as input, this principled ICRL approach outputs a constraint at least as constraining as the true underlying constraint with the specified level of confidence. Moreover, unlike previous methods, it can detect when the number of expert trajectories is insufficient to learn a constraint with the desired level of confidence, enabling users to collect more data if needed. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The ICRL method helps solve real-world problems by providing algorithms that estimate constraints from expert demonstrations. This allows us to know how confident we should be in our estimated constraints before using them. The new approach can take user-specified levels of confidence and expert demonstrations as input, giving us a constraint at least as good as the true one with the same level of confidence. It also tells us if we need more data to get the desired results. |
Keywords
* Artificial intelligence * Reinforcement learning
