Boosting for Control of Dynamical Systems
Naman Agarwal, Nataly Brukhim, Elad Hazan, Zhou Lu
2020年份
14被引次数
6顶会引用
摘要
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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引用它的顶会 Paper6
- A Boosting Approach to Reinforcement LearningNataly Brukhim, Elad Hazan, Karan SinghNeurIPS 2022 · 被引用 16 次
- Multiclass Boosting and the Cost of Weak LearningNataly Brukhim, Elad Hazan, Shay Moran, Indraneel Mukherjee 等NeurIPS 2021 · 被引用 16 次
- Smoothed Analysis with Adaptive AdversariesNika Haghtalab, Tim Roughgarden, Abhishek ShettyFOCS 2021 · 被引用 4 次
- Actor-Critic based Improper Reinforcement LearningMohammadi Zaki, Avi Mohan, Aditya Gopalan, Shie MannorICML 2022 · 被引用 4 次
- Online Agnostic Multiclass BoostingVinod Raman, Ambuj TewariNeurIPS 2022 · 被引用 3 次
相关 Paper
- Online Agnostic Boosting via Regret MinimizationNataly Brukhim, Xinyi Chen, Elad Hazan, Shay MoranNeurIPS 2020 · 被引用 16 次
- Boosting for Online Convex OptimizationElad Hazan, Karan SinghICML 2021 · 被引用 11 次
- A Regret Minimization Approach to Multi-Agent ControlUdaya Ghai, Udari Madhushani, Naomi Ehrich Leonard, Elad HazanICML 2022 · 被引用 6 次
- Boosting with Multiple SourcesCorinna Cortes, Mehryar Mohri, Dmitry Storcheus, Ananda Theertha SureshNeurIPS 2021 · 被引用 4 次
- Sample-Efficient Agnostic BoostingUdaya Ghai, Karan SinghNeurIPS 2024 · 被引用 3 次
