Summary of Partial Identifiability and Misspecification in Inverse Reinforcement Learning, by Joar Skalse and Alessandro Abate
Partial Identifiability and Misspecification in Inverse Reinforcement Learning
by Joar Skalse, Alessandro Abate
First submitted to arxiv on: 24 Nov 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 In this paper, researchers tackle the challenges of Inverse Reinforcement Learning (IRL), a technique that infers a reward function from a policy. The authors highlight two major hurdles in IRL: partial identifiability, where multiple reward functions can be compatible with a given policy, and misspecification, where the true relationship between human preferences and behavior is complex and difficult to model. To address these issues, the researchers provide a comprehensive mathematical analysis of partial identifiable and misspecification in IRL. They fully characterize and quantify the ambiguity of the reward function for common behavioral models used in IRL literature. Additionally, they introduce a framework for reasoning about partial identifiability and misspecification in IRL, along with tools to analyze new IRL models or reward learning algorithms. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Inverse Reinforcement Learning (IRL) is like trying to figure out why someone did something good or bad. It’s tricky because there might be many reasons why they chose a certain action, and we can’t always know for sure. In this paper, scientists studied how to make IRL better by understanding what makes it difficult. They found that two main problems are that there might not be just one “right” reason why someone did something, and that our models of human behavior aren’t perfect. The researchers then created a special way to understand these difficulties and developed tools to help make IRL more reliable. |
Keywords
* Artificial intelligence * Reinforcement learning
