Summary of Enhancing Policy Gradient with the Polyak Step-size Adaption, by Yunxiang Li et al.
Enhancing Policy Gradient with the Polyak Step-Size Adaption
by Yunxiang Li, Rui Yuan, Chen Fan, Mark Schmidt, Samuel Horváth, Robert M. Gower, Martin Takáč
First submitted to arxiv on: 11 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 The paper introduces a novel approach to reinforcement learning (RL) by integrating the Polyak step-size algorithm, which automatically adjusts the step-size without prior knowledge. The Polyak step-size is widely used in policy gradient RL due to its stability and convergence guarantees. However, its practical application has been hindered by sensitivity to hyper-parameters, particularly the step-size. To address this issue, the paper proposes adapting the Polyak step-size for RL settings, addressing unknown f* in the algorithm. The authors demonstrate the effectiveness of their approach through experiments, showing faster convergence and more stable policies. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper finds a way to make a popular learning method better by making it smarter about how it learns. It’s called reinforcement learning, and it helps machines decide what actions to take based on rewards or punishments. The problem is that this method often gets stuck if the “step-size” isn’t just right. The researchers came up with a new way to adjust the step-size without knowing beforehand what it should be. They tested this approach and found that it works better, allowing machines to learn faster and make more consistent decisions. |
Keywords
* Artificial intelligence * Reinforcement learning
