Summary of Statistical Analysis Of Policy Space Compression Problem, by Majid Molaei et al.
Statistical Analysis of Policy Space Compression Problem
by Majid Molaei, Marcello Restelli, Alberto Maria Metelli, Matteo Papini
First submitted to arxiv on: 15 Nov 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 A novel approach to accelerating reinforcement learning is proposed by reducing the policy space through compression. This technique condenses the original policy set into a smaller, representative subset while maintaining effectiveness. To determine the necessary sample size for accurate compressed policy estimation, Rényi divergence is employed to measure similarity between true and estimated policy distributions. The study focuses on establishing error bounds using both l_1 norm and model-based/model-free settings. Furthermore, correlations are explored between error bounds from different methods, shedding light on the required sample sizes for policies near vertices and those in the middle of the policy space. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Reinforcement learning can be slow because it searches through a huge space of possible actions. One way to speed things up is by squeezing this space down into a smaller set that still works well. This “policy compression” helps find the best actions faster. To figure out how much data we need to learn this compressed policy, we use special math tools like Rényi divergence. We also look at the l_1 norm and model-based/model-free settings to see what’s required for good approximations. |
Keywords
* Artificial intelligence * Reinforcement learning
