Summary of Functional Acceleration For Policy Mirror Descent, by Veronica Chelu and Doina Precup
Functional Acceleration for Policy Mirror Descent
by Veronica Chelu, Doina Precup
First submitted to arxiv on: 23 Jul 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 Medium Difficulty summary: This paper applies a novel technique called functional acceleration to the Policy Mirror Descent (PMD) family of algorithms in Reinforcement Learning (RL). By leveraging duality and proposing a momentum-based PMD update, the approach is independent of policy parametrization and applicable to large-scale optimization. Theoretical analysis shows several properties of this method, while an ablation study illustrates policy optimization dynamics on the value polytope relative to different algorithmic design choices. The paper also characterizes numerically features relevant for functional acceleration and investigates the impact of approximation on learning mechanics. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Low Difficulty summary: This research takes a new approach to machine learning called “functional acceleration” and applies it to a type of artificial intelligence called Reinforcement Learning (RL). RL helps computers make decisions by trial and error. The team’s method is special because it doesn’t rely on how the computer represents its ideas, making it suitable for big problems. They tested their idea and found some interesting patterns about how it works. |
Keywords
* Artificial intelligence * Machine learning * Optimization * Reinforcement learning
