Summary of Offline Inverse Rl: New Solution Concepts and Provably Efficient Algorithms, by Filippo Lazzati et al.
Offline Inverse RL: New Solution Concepts and Provably Efficient Algorithms
by Filippo Lazzati, Mirco Mutti, Alberto Maria Metelli
First submitted to arxiv on: 23 Feb 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 paper reframes inverse reinforcement learning (IRL) by estimating the feasible reward set instead of selecting a single reward function. The authors introduce a novel notion of feasible reward set for the offline setting, where an offline dataset is available, and propose two algorithms, IRLO and PIRLO, to address this problem. The latter algorithm enforces inclusion monotonicity of the delivered feasible set through pessimism. This work aims to provide a panorama of the challenges of the offline IRL problem and how they can be fruitfully addressed. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about using artificial intelligence to figure out what makes an expert agent “good” by looking at how it behaves. The problem is that there might be many different reasons why the expert is good, so the researchers came up with a new way of thinking about this problem. They want to know which possible reasons are most likely to be true, and they developed two special methods to help figure this out. One method is more conservative than the other, but both can help us understand what makes an expert agent good. |
Keywords
* Artificial intelligence * Reinforcement learning
