Summary of Convergence Of a L2 Regularized Policy Gradient Algorithm For the Multi Armed Bandit, by Stefana Anita and Gabriel Turinici
Convergence of a L2 regularized Policy Gradient Algorithm for the Multi Armed Bandit
by Stefana Anita, Gabriel Turinici
First submitted to arxiv on: 9 Feb 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Artificial Intelligence (cs.AI); Data Structures and Algorithms (cs.DS); Machine Learning (cs.LG); Numerical Analysis (math.NA)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 research paper investigates the theoretical properties of the policy gradient algorithm used for Multi Armed Bandit (MAB) problems with an L2 regularization term and softmax parametrization. The authors prove convergence under certain technical hypotheses and test the procedure numerically, including situations beyond the theoretical setting. The results show that a time-dependent regularized procedure can improve over the canonical approach, especially when the initial guess is far from the solution. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this paper, scientists study how to make computers learn by trying different options and choosing the best one. They look at two common ways to do this: Multi Armed Bandit (MAB) and policy gradient approach. The researchers focus on a special type of MAB that uses something called L2 regularization and softmax parametrization. They want to know if this way works well and can be improved. By doing some math and testing it with computers, they find out that this method can actually do better than the usual way when we start by guessing something that’s not quite right. |
Keywords
* Artificial intelligence * Regularization * Softmax
