Boosting for Control of Dynamical Systems
Naman Agarwal, Nataly Brukhim, Elad Hazan, Zhou Lu
2020Year
14Citations
6Top-tier citations
Abstract
We study the question of how to aggregate controllers for dynamical systems in order to improve their performance. To this end, we propose a framework of boosting for online control. Our main result is an efficient boosting algorithm that combines weak controllers into a provably more accurate one. Empirical evaluation on a host of control settings supports our theoretical findings.
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Install the CLIlune papers fulltext bcc70dc7-bb24-47af-86d7-8792429a4253Cited by top-tier papers6
- A Boosting Approach to Reinforcement LearningNataly Brukhim, Elad Hazan, Karan SinghNeurIPS 2022 · 16 citations
- Multiclass Boosting and the Cost of Weak LearningNataly Brukhim, Elad Hazan, Shay Moran, Indraneel Mukherjee et al.NeurIPS 2021 · 16 citations
- Smoothed Analysis with Adaptive AdversariesNika Haghtalab, Tim Roughgarden, Abhishek ShettyFOCS 2021 · 4 citations
- Actor-Critic based Improper Reinforcement LearningMohammadi Zaki, Avi Mohan, Aditya Gopalan, Shie MannorICML 2022 · 4 citations
- Online Agnostic Multiclass BoostingVinod Raman, Ambuj TewariNeurIPS 2022 · 3 citations
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- A Regret Minimization Approach to Multi-Agent ControlUdaya Ghai, Udari Madhushani, Naomi Ehrich Leonard, Elad HazanICML 2022 · 6 citations
- Boosting with Multiple SourcesCorinna Cortes, Mehryar Mohri, Dmitry Storcheus, Ananda Theertha SureshNeurIPS 2021 · 4 citations
- Sample-Efficient Agnostic BoostingUdaya Ghai, Karan SinghNeurIPS 2024 · 3 citations
