TY - JOUR
T1 - Efficient Online Linear Control with Stochastic Convex Costs and Unknown Dynamics
AU - Cassel, Asaf
AU - Cohen, Alon
AU - Koren, Tomer
N1 - Publisher Copyright:
© 2022 A. Cassel, A. Cohen & T. Koren.
PY - 2022
Y1 - 2022
N2 - We consider the problem of controlling an unknown linear dynamical system under a stochastic convex cost and full feedback of both the state and cost function. We present a computationally efficient algorithm that attains an optimal √T regret-rate compared to the best stabilizing linear controller in hindsight. In contrast to previous work, our algorithm is based on the Optimism in the Face of Uncertainty paradigm. This results in a substantially improved computational complexity and a simpler analysis.
AB - We consider the problem of controlling an unknown linear dynamical system under a stochastic convex cost and full feedback of both the state and cost function. We present a computationally efficient algorithm that attains an optimal √T regret-rate compared to the best stabilizing linear controller in hindsight. In contrast to previous work, our algorithm is based on the Optimism in the Face of Uncertainty paradigm. This results in a substantially improved computational complexity and a simpler analysis.
UR - http://www.scopus.com/inward/record.url?scp=85164708607&partnerID=8YFLogxK
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AN - SCOPUS:85164708607
SN - 2640-3498
VL - 178
SP - 3589
EP - 3604
JO - Proceedings of Machine Learning Research
JF - Proceedings of Machine Learning Research
T2 - 35th Conference on Learning Theory, COLT 2022
Y2 - 2 July 2022 through 5 July 2022
ER -