by S. S. Ramesh, P. G. Sessa, A. Krause, I. Bogunovic
Abstract:
Contextual Bayesian optimization (CBO) is a powerful framework for sequential decision-making given side information, with important applications, e.g., in wind energy systems. In this setting, the learner receives context (e.g., weather conditions) at each round, and has to choose an action (e.g., turbine parameters). Standard algorithms assume no cost for switching their decisions at every round. However, in many practical applications, there is a cost associated with such changes, which should be minimized. We introduce the episodic CBO with movement costs problem and, based on the online learning approach for metrical task systems of Coester and Lee (2019), propose a novel randomized mirror descent algorithm that makes use of Gaussian Process confidence bounds. We compare its performance with the offline optimal sequence for each episode and provide rigorous regret guarantees. We further demonstrate our approach on the important real-world application of altitude optimization for Airborne Wind Energy Systems. In the presence of substantial movement costs, our algorithm consistently outperforms standard CBO algorithms.
Reference:
Movement Penalized Bayesian Optimization with Application to Wind Energy Systems S. S. Ramesh, P. G. Sessa, A. Krause, I. BogunovicIn Proc. Neural Information Processing Systems (NeurIPS), 2022
Bibtex Entry:
@inproceedings{ramesh22movement,
author = {Shyam Sundhar Ramesh and Pier Giuseppe Sessa and Andreas Krause and Ilija Bogunovic},
booktitle = {Proc. Neural Information Processing Systems (NeurIPS)},
month = {December},
title = {Movement Penalized Bayesian Optimization with Application to Wind Energy Systems},
year = {2022}}