Summary of Cross-validated Off-policy Evaluation, by Matej Cief et al.
Cross-Validated Off-Policy Evaluation
by Matej Cief, Branislav Kveton, Michal Kompan
First submitted to arxiv on: 24 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 paper explores the application of estimator selection and hyper-parameter tuning techniques in off-policy evaluation, a crucial aspect of reinforcement learning. The authors demonstrate how to adapt cross-validation methods from supervised learning to off-policy evaluation, dispelling the notion that this approach is not feasible. The study evaluates the effectiveness of their method across various use cases, providing insights for practitioners working with off-policy evaluation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Off-policy evaluation is a technique used in reinforcement learning to evaluate the performance of an agent without direct access to the reward function. In this paper, researchers investigate how to select the best estimator and tune hyperparameters for off-policy evaluation. They find that cross-validation methods from supervised learning can be applied to off-policy evaluation, making it easier for practitioners to choose the right approach for their task. |
Keywords
» Artificial intelligence » Reinforcement learning » Supervised
