Summary of Towards Principled, Practical Policy Gradient For Bandits and Tabular Mdps, by Michael Lu et al.
Towards Principled, Practical Policy Gradient for Bandits and Tabular MDPs
by Michael Lu, Matin Aghaei, Anant Raj, Sharan Vaswani
First submitted to arxiv on: 21 May 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 explores stochastic softmax policy gradient (PG) methods for bandits and tabular Markov decision processes (MDPs). The authors develop practical and principled PG methods in both exact and stochastic settings. In the exact setting, an Armijo line-search is employed to set the step-size, demonstrating a linear convergence rate. In the stochastic setting, exponentially decreasing step-sizes are utilized, characterizing the convergence rate of the resulting algorithm. The proposed algorithms offer similar theoretical guarantees as state-of-the-art results without requiring oracle-like quantities. For multi-armed bandits, the techniques result in a theoretically-principled PG algorithm that does not require explicit exploration or knowledge of reward distributions. Finally, empirical comparisons are made to PG approaches that require oracle knowledge, demonstrating competitive performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper explores new ways to make decisions when there’s uncertainty involved. It looks at algorithms called policy gradient methods and finds a way to make them work better without needing to know specific details about the problem. This is helpful because it means we can use these algorithms in more situations without having to make big assumptions. The paper also shows that the new algorithm works well compared to other similar ones. |
Keywords
» Artificial intelligence » Softmax
