基于情景依赖选择模拟的统一离线-在线学习范式

Haitao Liu, Xiao Jin, Haobin Li, L. Lee, E. P. Chew
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引用次数: 1

摘要

仿真主要用于解决离线静态系统设计问题,由于在线决策时间紧迫,基于仿真的在线决策一直是一个弱点。本文通过考虑在线场景和预算,扩展了基于场景的排序和选择模型。我们提出了一个统一的离线-在线学习(UOOL)范式,通过模拟来找到在线场景的最佳替代条件。其思想是离线学习场景与平均性能之间的关系,然后根据学习到的预测模型和在线场景信息动态分配在线仿真预算。通过与人工神经网络和决策树的比较,在四个测试函数上验证了UOOL范式的优越性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Unified Offline-Online Learning Paradigm via Simulation for Scenario-Dependent Selection
Simulation has primarily been used for offline static system design problems, and the simulation-based online decision making has been a weakness as the online decision epoch is tight. This work extends the scenario-dependent ranking and selection model by considering online scenario and budget. We propose a unified offline-online learning (UOOL) paradigm via simulation to find the best alternative conditional on the online scenario. The idea is to offline learn the relationship between scenarios and mean performance, and then dynamically allocates the online simulation budget based on the learned predictive model and online scenario information. The superior performance of UOOL paradigm is validated on four test functions by comparing it with artificial neural networks and decision tree.
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