Efficient Online Linear Control with Stochastic Convex Costs and Unknown Dynamics
Optimization and Control
2022-06-23 v2 Machine Learning
Machine Learning
Abstract
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 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.
Cite
@article{arxiv.2203.01170,
title = {Efficient Online Linear Control with Stochastic Convex Costs and Unknown Dynamics},
author = {Asaf Cassel and Alon Cohen and Tomer Koren},
journal= {arXiv preprint arXiv:2203.01170},
year = {2022}
}