Summary of Opera: Automatic Offline Policy Evaluation with Re-weighted Aggregates Of Multiple Estimators, by Allen Nie et al.
OPERA: Automatic Offline Policy Evaluation with Re-weighted Aggregates of Multiple Estimators
by Allen Nie, Yash Chandak, Christina J. Yuan, Anirudhan Badrinath, Yannis Flet-Berliac, Emma Brunskil
First submitted to arxiv on: 27 May 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 The proposed algorithm adaptively blends multiple offline policy evaluation (OPE) estimators to provide a consistent estimate of a new sequential decision-making policy’s performance without requiring explicit hyperparameter tuning or training. The paper contributes to improving ease of use for a general-purpose, estimator-agnostic, off-policy evaluation framework for offline reinforcement learning. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new algorithm is proposed that blends multiple OPE estimators together. This helps solve the problem of choosing the best OPE algorithm for each task and domain. The algorithm doesn’t require any extra tuning or training and can be used to pick better policies in different areas like healthcare and robotics. |
Keywords
* Artificial intelligence * Hyperparameter * Reinforcement learning
