结构化强盗问题的全局信息汤普森抽样及其在众转码中的应用

Xingchi Liu, Mahsa Derakhshani, Ziming Zhu, S. Lambotharan
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引用次数: 0

摘要

多臂强盗模型是一个被广泛研究的序列决策模型。文献中研究最多的模型是随机强盗,其中每个手臂的奖励遵循独立分布。然而,在许多应用中,不同选择的回报在某种程度上是相关的。本文研究了一类结构强盗问题,其中不同臂的奖励是同一未知参数向量的函数。为了最大限度地减少累积学习遗憾,我们提出了一种全局信息汤普森采样算法来学习和利用臂间的相关性,该算法可以处理未知的多维参数和非单调奖励函数。我们的研究表明,该算法在学习速度上取得了显著的提高。特别地,所设计的算法用于解决众包视频直播系统中的边缘转码器选择问题,与现有方案相比显示出优越的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Globally Informative Thompson Sampling for Structured Bandit Problems with Application to CrowdTranscoding
Multi-armed bandit is a widely-studied model for sequential decision-making problems. The most studied model in the literature is stochastic bandits wherein the reward of each arm follows an independent distribution. However, there is a wide range of applications where the rewards of different alternatives are correlated to some extent. In this paper, a class of structured bandit problems is studied in which rewards of different arms are functions of the same unknown parameter vector. To minimize the cumulative learning regret, we propose a globally-informative Thompson sampling algorithm to learn and leverage the correlation among arms, which can deal with unknown multi-dimensional parameter and non-monotonic reward functions. Our studies demonstrate that the proposed algorithm achieves significant improvement in the learning speed. In particular, the designed algorithm is used to solve an edge transcoder selection problem in crowdsourced live video streaming systems and shows superior performance as compared to the existing schemes.
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