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We study the question of how to aggregate controllers for dynamical systems in order to improve their performance.
A decision-theoretic generalization of on-line learning and an application to boosting
Yoav Freund and Robert E Schapire · 1997
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Boosting algorithms as gradient descent
Llew Mason, Jonathan Baxter, Peter L Bartlett, and Marcus R Frean · 2000
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An efficient boosting algorithm for combining preferences
Yoav Freund, Raj Iyer, Robert E. Schapire, and Yoram Singer · 2003
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Potential-based agnostic boosting
Varun Kanade and Adam Kalai · 2009
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Boosting: Foundations and algorithms
Robert E Schapire and Yoav Freund · 2012
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Online learning for adversaries with memory: price of past mistakes
Oren Anava, Elad Hazan, and Shie Mannor · 2015
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Alina Beygelzimer, Satyen Kale, and Haipeng Luo · 2015
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Openai gym, 2016
Ludwig Pettersson Jonas Schneider John Schulman Jie Tang Greg Brockman, Vicki Cheung and Wojciech Zaremba · 2016
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Online linear quadratic control
Alon Cohen, Avinatan Hasidim, Tomer Koren, Nevena Lazic, Yishay Mansour, and Kunal Talwar · 2018
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Online control with adversarial disturbances
Naman Agarwal, Brian Bullins, Elad Hazan, Sham M Kakade, and Karan Singh · 2019
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Learning linear-quadratic regulators efficiently with only T \sqrt{T} regret
Alon Cohen, Tomer Koren, and Yishay Mansour · 2019
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The nonstochastic control problem
Elad Hazan, Sham M Kakade, and Karan Singh · 2019
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Improper learning for non-stochastic control, 2020
Max Simchowitz, Karan Singh, and Elad Hazan · 2020
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