# Boosting for Control of Dynamical Systems

arXiv: https://arxiv.org/abs/1906.08720 · HTML: https://mlsanity.com/paper/1906.08720/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Jun 2019.

## Built on by (most cited first, 7 of 7)

- [Smoothed Analysis with Adaptive Adversaries](https://mlsanity.com/paper/2102.08446/) (2021)
- [A Boosting Approach to Reinforcement Learning](https://mlsanity.com/paper/2108.09767/) (2021)
- [Regret Analysis of Learning-Based MPC with Partially-Unknown Cost Function](https://mlsanity.com/paper/2108.02307/) (2021)
- [Online Agnostic Multiclass Boosting](https://mlsanity.com/paper/2205.15113/) (2022)
- [Actor-Critic based Improper Reinforcement Learning](https://mlsanity.com/paper/2207.09090/) (2022)
- [Online Residual Learning from Offline Experts for Pedestrian Tracking](https://mlsanity.com/paper/2409.04069/) (2024)
- [Improper Reinforcement Learning with Gradient-based Policy Optimization](https://mlsanity.com/paper/2102.08201/) (2021)

## Abstract (arXiv)

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.