Summary of Inverse Reinforcement Learning with Sub-optimal Experts, by Riccardo Poiani et al.
Inverse Reinforcement Learning with Sub-optimal Experts
by Riccardo Poiani, Gabriele Curti, Alberto Maria Metelli, Marcello Restelli
First submitted to arxiv on: 8 Jan 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 This research paper extends Inverse Reinforcement Learning (IRL) techniques to problems where multiple sub-optimal experts are observed, unlike traditional IRL which focuses on a single optimal expert. The study explores how the presence of these sub-optimal experts affects the feasible reward set, finding that it can significantly shrink this set. Additionally, the paper analyzes the statistical complexity of estimating the feasible reward set using a generative model and a uniform sampling algorithm, showing minimax optimality when the performance level of the sub-optimal experts is close to that of the optimal agent. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research takes Inverse Reinforcement Learning (IRL) to the next level by studying how multiple sub-optimal experts can help or hinder our understanding of a reward function. Usually, IRL looks at one super-skilled expert, but what if we have different levels of skill? This paper shows that having many experts with varying skills can actually make it harder to figure out the right reward function. It’s like trying to solve a puzzle with many pieces that don’t quite fit together. |
Keywords
* Artificial intelligence * Generative model * Reinforcement learning
