Summary of Towards the Transferability Of Rewards Recovered Via Regularized Inverse Reinforcement Learning, by Andreas Schlaginhaufen et al.
Towards the Transferability of Rewards Recovered via Regularized Inverse Reinforcement Learning
by Andreas Schlaginhaufen, Maryam Kamgarpour
First submitted to arxiv on: 3 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (stat.ML)
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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 paper investigates inverse reinforcement learning (IRL), which aims to infer a reward function from expert demonstrations. The authors highlight the importance of transferring learned rewards to new environments, but current methods assume full access to the expert’s policy, limiting their practicality. To address this challenge, the researchers propose principal angles as a measure of transition law similarity and dissimilarity. They establish two key results: (1) sufficient conditions for transferability to any transition laws when learning from multiple experts with diverse transition laws; and (2) sufficient conditions for transferability to local changes in the transition law when learning from a single expert. The authors also provide a probably approximately correct (PAC) algorithm and an end-to-end analysis for learning transferable rewards. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how we can learn a reward function from watching someone else do something. It’s like trying to figure out what makes a task worth doing just by seeing someone do it well. The problem is that the same reward might not work in different situations, so the authors wanted to find ways to make sure the learned reward works in new situations too. They came up with a new way of measuring how similar or different two situations are, and they showed that this can help us learn rewards that will work well in new situations. They also developed an algorithm and proved it works well for learning transferable rewards. |
Keywords
» Artificial intelligence » Reinforcement learning » Transferability
