Summary of Inverse Reinforcement Learning by Estimating Expertise Of Demonstrators, By Mark Beliaev et al.
Inverse Reinforcement Learning by Estimating Expertise of Demonstrators
by Mark Beliaev, Ramtin Pedarsani
First submitted to arxiv on: 2 Feb 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 paper introduces IRLEED (Inverse Reinforcement Learning by Estimating Expertise of Demonstrators), a novel framework that addresses the challenge of utilizing suboptimal and heterogeneous demonstrations in Imitation Learning. Standard IL algorithms consider these datasets as homogeneous, inheriting the deficiencies of suboptimal demonstrators. The proposed approach enhances existing IRL algorithms by combining a general model for demonstrator suboptimality to address reward bias and action variance, with a Maximum Entropy IRL framework to efficiently derive the optimal policy from diverse, suboptimal demonstrations. Experiments in both online and offline IL settings demonstrate IRLEED’s adaptability and effectiveness. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us learn from people who aren’t perfect at teaching. In imitation learning, we use imperfect data from others to teach a machine how to do something. But when the teachers are not very good or are inconsistent, it can be hard for the machine to learn well. The authors of this paper have created a new way to deal with this problem called IRLEED. It’s like a super-smart teacher who can take in all kinds of teaching styles and make sense of them. The results show that their method works really well and can be used in different situations, which is important because real-world data can be messy and varied. |
Keywords
* Artificial intelligence * Reinforcement learning
