Boosting for Control of Dynamical Systems
Naman Agarwal ⋅ Nataly Brukhim ⋅ Elad Hazan ⋅ Zhou Lu
Keywords:
Planning and Control
Boosting / Ensemble Methods
Online Learning / Bandits
Planning, Control, and Multiagent Learning
2020 Poster
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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