Summary of Two-way Deconfounder For Off-policy Evaluation in Causal Reinforcement Learning, by Shuguang Yu et al.
Two-way Deconfounder for Off-policy Evaluation in Causal Reinforcement Learning
by Shuguang Yu, Shuxing Fang, Ruixin Peng, Zhengling Qi, Fan Zhou, Chengchun Shi
First submitted to arxiv on: 8 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: 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 paper proposes a novel approach to off-policy evaluation (OPE) in reinforcement learning settings where unmeasured confounders are present. Building on two-way fixed effects regression models, it develops a deconfounder algorithm that leverages neural tensor networks to learn both the confounders and system dynamics. This allows for consistent policy value estimation using model-based methods. Theoretical results and numerical experiments demonstrate the effectiveness of the proposed approach. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper studies how to accurately measure the quality of decisions made by artificial intelligence systems, even when there are unknown factors influencing their choices. It introduces a new method that takes into account these hidden factors and uses it to improve how well AI systems can estimate the value of different actions. This is important for ensuring that AI systems make good decisions in real-world situations. |
Keywords
» Artificial intelligence » Regression » Reinforcement learning
