Boosting for Control of Dynamical Systems
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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.
Key facts. Cited by 7 papers in the map (data through 14 Oct 2026). First posted on arXiv 20 Jun 2019.
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Citing papers per year: 2021: 4; 2022: 2; 2023: 0; 2024: 1.
7 papers.
- 2021Smoothed Analysis with Adaptive Adversariescited by 57
- 2021A Boosting Approach to Reinforcement Learningcited by 12
- 2021Regret Analysis of Learning-Based MPC with Partially-Unknown Cost Functioncited by 5
- 2022Online Agnostic Multiclass Boostingcited by 3
- 2022Actor-Critic based Improper Reinforcement Learningcited by 3
- 2024Online Residual Learning from Offline Experts for Pedestrian Trackingcited by 1
- 2021Improper Reinforcement Learning with Gradient-based Policy Optimizationcited by 0
- 2024Online Residual Learning from Offline Experts for Pedestrian Tracking6 Sep 2024
- 2022Actor-Critic based Improper Reinforcement Learning19 Jul 2022
- 2022Online Agnostic Multiclass Boosting30 May 2022
- 2021A Boosting Approach to Reinforcement Learning22 Aug 2021
- 2021Regret Analysis of Learning-Based MPC with Partially-Unknown Cost Function4 Aug 2021
- 2021Smoothed Analysis with Adaptive Adversaries16 Feb 2021
- 2021Improper Reinforcement Learning with Gradient-based Policy Optimization16 Feb 2021