一个快速的算法,用于真实价值的组合纯探索的多武器强盗
Shintaro Nakamura1,2, Masashi Sugiyama3,4
1The University of Tokyo, Bunkyo-ku, Tokyo 113-8654, Japan.
Neural computation
|December 2, 2024
概括
我们介绍了CombGapE算法,用于实值组合的纯探索问题在随机多臂盗. 这种新方法实现了最佳的样本复杂性,并且在模拟和现实世界测试中优于现有的方法.
科学领域:
- 机器学习 机器学习
- 优化优化 优化优化
- 强化学习是一种强化学习.
背景情况:
- 实值组合纯勘探问题 (R-CPE-MAB) 是随机多武器强盗设置中的一个关键挑战.
- 现有的方法在实践应用中常见的大型行动集中扎.
研究的目的:
- 开发一个高效的算法 R-CPE-MAB 问题与多项式动作集大小.
- 为拟议的算法建立理论性能保证.
- 证明新算法在现有方法上的实际优势.
主要方法:
- 介绍基于差距的组合勘探 (CombGapE) 算法.
- 分析了CombGapE的样本复杂性,显示它与理论下限相匹配.
- 在合成和现实数据集上进行数值实验.
主要成果:
- CombGapE算法在样本复杂性的上限与下限相匹配,达到一个常数因子.
- 数字结果表明,与现有算法相比,CombGapE的性能得到了显著改善.
- 算法的有效性在模拟和现实数据上得到验证.
结论:
- 对于R-CPE-MAB问题,CombGapE提供了一个理论上有根据的,实际上有效的解决方案.
- 该算法在随机多臂强盗领域取得了重大进展,特别是在大型动作集的问题上.
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