Learning Personalized Optimal Control for Repeatedly Operated Systems
Abstract
We consider the problem of online learning of optimal control for repeatedly operated systems in the presence of parametric uncertainty. During each round of operation, environment selects system parameters according to a fixed but unknown probability distribution. These parameters govern the dynamics of a plant. An agent chooses a control input to the plant and is then revealed the cost of the choice. In this setting, we design an agent that personalizes the control input to this plant taking into account the stochasticity involved. We demonstrate the effectiveness of our approach on a simulated system.
Cite
@article{arxiv.1609.05536,
title = {Learning Personalized Optimal Control for Repeatedly Operated Systems},
author = {Theja Tulabandhula},
journal= {arXiv preprint arXiv:1609.05536},
year = {2016}
}
Comments
This work was presented at the NIPS 2015 Workshop: Machine Learning From and For Adaptive User Technologies: From Active Learning & Experimentation to Optimization & Personalization (ref. https://sites.google.com/site/mlaihci)