Summary of Fully Adaptive Regret-guaranteed Algorithm For Control Of Linear Quadratic Systems, by Jafar Abbaszadeh Chekan and Cedric Langbort
Fully Adaptive Regret-Guaranteed Algorithm for Control of Linear Quadratic Systems
by Jafar Abbaszadeh Chekan, Cedric Langbort
First submitted to arxiv on: 11 Jun 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG); Systems and Control (eess.SY)
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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 In this paper, researchers aim to develop an algorithm for the Linear Quadratic (LQ) control problem that can adapt to unknown system models while maintaining a regret of O(sqrt(T)). Building upon previous works, they propose a fully adaptive algorithm that adjusts the exploration-exploitation trade-off and optimizes the upper-bound of regret. The new approach relaxes the need for a horizon-dependent warm-up phase by tuning the regularization parameter and adding an adaptive input perturbation. This paper’s main contribution is to show that careful exploration-exploitation trade-off adjustment allows for strong sequential stability without requiring initialization complexities. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a new algorithm for controlling unknown system models while keeping regret low. It tries to make a good balance between trying new things and sticking with what works. The researchers take some ideas from previous work and add their own twist to make it better. They show that this new way of doing things doesn’t need extra setup or special handling. |
Keywords
» Artificial intelligence » Regularization
