Summary of Optimistic Online Non-stochastic Control Via Ftrl, by Naram Mhaisen and George Iosifidis
Optimistic Online Non-stochastic Control via FTRL
by Naram Mhaisen, George Iosifidis
First submitted to arxiv on: 4 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Optimization and Control (math.OC)
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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 new approach to Non-stochastic Control (NSC) by applying optimistic learning with delayed feedback techniques. It proposes the Optimistic Follow the Regularized Leader (OFTRL) algorithmic family, which is used to design the first Disturbance Action Controller (DAC) with optimistic policy regret bounds. The DAC, called OptFTRL-C, has bounds that are commensurate with the oracle’s accuracy, ranging from O(1) for perfect predictions to O(sqrt(T)) when all predictions fail. This work contributes to the advancement of the NSC framework and enables effective and robust learning-based controllers. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper talks about a new way to control things online without knowing what will happen in advance. It’s like trying to drive a car without knowing where you’re going, but with some help from an unknown friend who gives you hints about how far away you are from your destination. The authors use special math tricks called optimistic learning and delayed feedback to make sure they can still make good decisions even when the hints are wrong. This helps them design a new kind of controller that works well even when it doesn’t have all the information. |
