Summary of Potec: Off-policy Learning For Large Action Spaces Via Two-stage Policy Decomposition, by Yuta Saito et al.
POTEC: Off-Policy Learning for Large Action Spaces via Two-Stage Policy Decomposition
by Yuta Saito, Jihan Yao, Thorsten Joachims
First submitted to arxiv on: 9 Feb 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 tackles off-policy learning (OPL) of contextual bandit policies in large discrete action spaces, where existing methods fail due to excessive bias or variance. The authors propose a novel two-stage algorithm called Policy Optimization via Two-Stage Policy Decomposition (POTEC), which leverages clustering in the action space and learns two different policies via policy-based and regression-based approaches. POTEC uses a low-variance gradient estimator for efficient learning of the first-stage policy, followed by a second-stage policy derived from a regression-based approach within each cluster. The authors demonstrate that POTEC provides substantial improvements in OPL effectiveness, particularly in large and structured action spaces. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Off-policy learning is a challenging problem when it comes to contextual bandit policies in large action spaces. This paper introduces a new algorithm called POTEC (Policy Optimization via Two-Stage Policy Decomposition) that solves this issue by using clustering and two different policy-based approaches. It’s like a step-by-step process: first, it finds the right cluster to take an action from, then it chooses the best action within that cluster. This helps with making good decisions without getting stuck in one place. |
Keywords
* Artificial intelligence * Clustering * Optimization * Regression
