Movement Penalized Bayesian Optimization with Application to Wind Energy Systems
This work addresses the practical issue of minimizing switching costs in sequential decision-making for applications like wind energy systems, representing an incremental improvement over existing contextual Bayesian optimization methods.
The paper tackles the problem of contextual Bayesian optimization with movement costs, where switching decisions incurs a penalty, and introduces a novel randomized mirror descent algorithm that outperforms standard methods in scenarios with substantial costs, as demonstrated in altitude optimization for airborne wind energy systems.
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.