这一类方法叫 Pareto Front Learning (PFL), Pareto Set Learning (PSL) 都可以。 优势: 应对偏好trade-off提前不可知的情况 应对偏好trade-off需要实时变动的情况 仅有限增加模型代价 多目标背景 在现实生活中,我们经常在做决策时既要考虑价格,又要考虑质量;深度学习模型里往往既要考虑准确率,又要考虑泛化性。这...
pareto front: Set中每个解对应的目标值向量组成的集合,成为pareto front 三、算法简介 图2(原图3).将偏好映射到帕累托前沿 Pareto set model 在该方法中,建立一个集合模型,将任何权衡偏好映射到它们相应的帕累托解决方案与比例化。 最基本的建立偏好到解之间的关系公式如下: 标量化技术将多目标问题转换为单目标...
Pareto front learning is a technique that introduces preference vectors in a neural network to approximate the Pareto front. Previous Pareto front learning methods have demonstrated high performance in approximating simple Pareto fronts. These methods often sample preference vectors from a fixed Dirichlet ...
We consider Pareto front identification (PFI) for linear bandits (PFILin), i.e., the goal is to identify a set of arms with undominated mean reward vectors when the mean reward vector is a linear function of the context. PFILin includes the best arm identification problem and multi-...
Learning the Pareto Front with HypernetworksAviv NavonAviv ShamsianGal ChechikEthan FetayaInternational Conference on Learning Representations
Most reinforcement learning research aims to optimize agents' policies for a single objective. However, many real-world applications are inherently characterized by the presence of multiple, possibly conflicting, objectives. As a genera...
Active Learning of Pareto Fronts. This paper introduces the active learning of Pareto fronts (ALP) algorithm, a novel approach to recover the Pareto front of a multiobjective optimization p... Campigotto,Paolo,Passerini,... - 《IEEE Transactions on Neural Networks & Learning Systems》 被引量:...
Pareto front是指不能使所有目标同时提高的解集。通过研究与Pareto front 相近的或者改进后的概念,研究者们给出了各种算法,去寻找满足Pareto front的解集。如P.Loridan (1984)19, Papadimitriou, C.H.和Yannakakis, M (2000)20等。而Yonatan Auman 9、和Yair Dombb(2010)则是沿用了很多研究纳什均衡和强纳什...
求解Pareto+Front多目标遗传算法分析.pdf,摘要 遗传算法是模拟达尔文的遗传选择和自然淘汰的生物进化 过程的一种新的迭代的全局优化搜索算法,已经广泛地应用到组合 优化问题求解、自适应控制、规划设计、机器学习和人工生命等领 域。由于现实世界中存在的问题往往呈现为多
为了协调和结合这些不同的目标,作者比较了两种不同的奖励方案:Pareto front(PF)和weighted sum(WS)。 PF方案根据不同解集之间的支配关系,确定有优势的解集。例如,给定一个问题的两个解决方案m1,m2,其解集分别为(x1,x2,…,xn)和(y1,y2,…,yn),对于∀ i∈{1,2,…,n} 都有xi≥yi 且 ∃ i∈{1,...