运动惩罚贝叶斯优化及其在风能系统中的应用
Movement Penalized Bayesian Optimization with Application to Wind Energy Systems
摘要：
上下文贝叶斯优化（CBO）是一个强大的框架，用于在给定边信息的情况下进行顺序决策，具有重要的应用，例如，在风能系统中。在这种设置中，学习者接收上下文（例如，天气条件），并且必须选择动作（例如，涡轮机参数）。标准算法假定在每一轮切换决策没有成本。然而，在许多实际应用中，存在与这种改变相关联的成本，这应该被最小化。我们引入了具有移动成本问题的情节CBO，并基于Coester和Lee（2019）的度量任务系统的在线学习方法，提出了一种新的随机镜像下降算法，该算法利用了高斯过程置信界。我们比较其性能与离线的最佳序列为每一集，并提供严格的遗憾保证。我们进一步展示了我们的方法在机载风能系统高度优化的重要现实应用。在大量的运动成本的存在下，我们的算法始终优于标准的CBO算法。
英文摘要：
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.
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