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Oct, 2024
可扩展的核逆优化
Scalable Kernel Inverse Optimization
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Youyuan Long, Tolga Ok, Pedro Zattoni Scroccaro, Peyman Mohajerin Esfahani
TL;DR
本研究解决了逆优化中目标函数学习的不足,提出了一种基于再生核希尔伯特空间(RKHS)的新方法,增强了特征表示能力。通过引入序列选择优化(SSO)算法,实现了对核逆优化模型的有效训练,并在MuJoCo基准上验证了该模型的推广能力和SSO算法的有效性。
Abstract
Inverse Optimization
(IO) is a framework for learning the unknown objective function of an expert decision-maker from a past dataset. In this paper, we extend the hypothesis class of IO objective functions to a
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