Summary of Percentile Criterion Optimization in Offline Reinforcement Learning, by Elita A. Lobo et al.
Percentile Criterion Optimization in Offline Reinforcement Learning
by Elita A. Lobo, Cyrus Cousins, Yair Zick, Marek Petrik
First submitted to arxiv on: 7 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 In reinforcement learning, designing robust policies for high-stakes decision-making problems with limited data is crucial. The traditional approach optimizes the percentile criterion by constructing an ambiguity set containing the true model with high probability and optimizing the policy for the worst-case scenario within that set. However, existing methods using Bayesian credible regions often result in overly conservative policies due to unnecessarily large ambiguity sets. To address this issue, we introduce a novel Value-at-Risk based dynamic programming algorithm that optimizes the percentile criterion without explicitly constructing ambiguity sets. Our results demonstrate that this approach implicitly generates smaller ambiguity sets and learns more robust yet less conservative policies. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In reinforcement learning, finding good policies for hard decisions with limited data is important. Usually, people try to optimize a “percentile” goal by making an “ambiguity set” that probably includes the real situation, then choosing the best option within that set. However, existing methods often make sets too big and end up with overly cautious choices. To fix this, we created a new algorithm using Value-at-Risk that optimizes the percentile goal without making those sets. Our work shows that this method makes smaller sets and learns better policies. |
Keywords
» Artificial intelligence » Probability » Reinforcement learning
