Summary of Automated Feature Selection For Inverse Reinforcement Learning, by Daulet Baimukashev et al.
Automated Feature Selection for Inverse Reinforcement Learning
by Daulet Baimukashev, Gokhan Alcan, Ville Kyrki
First submitted to arxiv on: 22 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Robotics (cs.RO)
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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 proposes a novel inverse reinforcement learning (IRL) approach that uses polynomial basis functions to form a candidate set of features, allowing the matching of statistical moments of state distributions. The method addresses the challenge of selecting relevant features in continuous state spaces, where the state variables alone are insufficient as features. By leveraging correlation between trajectory probabilities and feature expectations, the approach is shown to be effective in recovering reward functions that capture expert policies across non-linear control tasks of increasing complexity. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper develops a new way to learn reward functions from expert demonstrations using inverse reinforcement learning (IRL). Instead of manually specifying rewards, IRL can automatically learn what makes an action good or bad. The researchers found that in complex situations where many things are changing at once, it’s hard to choose the right features to use as clues about how well actions worked out. They came up with a clever way to select the best features by looking at how likely different paths through the situation were and what features were most important for predicting those paths. |
Keywords
* Artificial intelligence * Reinforcement learning
