对于具有非线性强盗反和多样性约束的Top-K多武器强盗,主-奴隶深层架构
IEEE transactions on neural networks and learning systems
|November 24, 2023
概括
我们引入了一种新的主-奴隶方法,用于具有非线性反和多样性约束的top-K组合式多臂强盗 (CMAB). 这种方法增强了勘探和开发,以提高推系统的性能.
科学领域:
- 机器学习 机器学习
- 人工智能的人工智能
- 优化优化 优化优化
背景情况:
- 组合式多武器盗 (CMABs) 对于顺序决策至关重要.
- 现有的CMAB经常缺乏有效处理非线性反和多样性约束.
- 解决这些局限性是改善推系统的关键.
研究的目的:
- 为顶级-K CMABs问题提出了一个新的主-奴隶架构.
- 有效地纳入非线性盗反和多样性约束.
- 在复杂的行动空间中增强勘探-开发权衡.
主要方法:
- 开发了一个主-奴隶架构,有六个专门的奴隶模型.
- 实现了基于教师学习的优化,并为奴隶模型提供政策联合培训.
- 使用基于NeuralUCB的总体模型进行样本选择和决策.
主要成果:
- 提出的方法显著超过了最先进的算法.
- 在合成和现实世界推数据集上都表现出卓越的性能.
- 该架构有效地平衡了奖励,约束和勘探效率.
结论:
- 这种新的主-奴隶架构对于具有多样性约束的顶级K CMABs 非常有效.
- 专门的奴隶模型和主奴互动的组合产生了显著的性能增长.
- 这项工作为推系统中复杂的盗问题提供了一个强大的框架.
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