Summary of Improved Sample Complexity For Global Convergence Of Actor-critic Algorithms, by Navdeep Kumar et al.
Improved Sample Complexity for Global Convergence of Actor-Critic Algorithms
by Navdeep Kumar, Priyank Agrawal, Giorgia Ramponi, Kfir Yehuda Levy, Shie Mannor
First submitted to arxiv on: 11 Oct 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 This paper presents significant advances in the actor-critic algorithm, establishing global convergence with improved sample complexity. Building upon previous local convergence results, the authors demonstrate that a constant step size for the critic is sufficient to ensure convergence in expectation, contrary to traditional methods that employ decreasing step sizes for both components. This key insight highlights the importance of using a decreasing step size for the actor alone to handle noise, providing theoretical support for the practical success of many algorithms. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper shows how to make an algorithm called the actor-critic algorithm work better by making some changes. Before, this algorithm only worked locally, but now it can work globally with fewer samples needed. This is important because it means we can use this algorithm to learn from data more efficiently. The authors found that using a constant step size for one part of the algorithm and decreasing steps for another part helps it converge faster. |
