论文标题
CVAR土匪的风险约束汤普森抽样
Risk-Constrained Thompson Sampling for CVaR Bandits
论文作者
论文摘要
多臂强盗（MAB）问题是一个无处不在的决策问题，体现了探索探索 - 探索权的权衡。标准配方排除了决策的风险。风险显然使基本的奖励最大化目标复杂化，部分原因是对此没有普遍同意的定义。在本文中，我们考虑了一种流行的定量融资风险措施，称为有条件价值的风险（CVAR）。我们探讨了在此风险度量下，基于汤普森采样的算法CVAR-TS的性能。我们在可比的设置中与最先进的L/UCB算法之间的遗憾界限与最先进的算法之间提供了全面的比较，并证明了它们在性能方面的明显改善。我们还包括数值模拟，以经验验证CVAR-TS的表现优于其他基于L/UCB的算法。
The multi-armed bandit (MAB) problem is a ubiquitous decision-making problem that exemplifies the exploration-exploitation tradeoff. Standard formulations exclude risk in decision making. Risk notably complicates the basic reward-maximising objective, in part because there is no universally agreed definition of it. In this paper, we consider a popular risk measure in quantitative finance known as the Conditional Value at Risk (CVaR). We explore the performance of a Thompson Sampling-based algorithm CVaR-TS under this risk measure. We provide comprehensive comparisons between our regret bounds with state-of-the-art L/UCB-based algorithms in comparable settings and demonstrate their clear improvement in performance. We also include numerical simulations to empirically verify that CVaR-TS outperforms other L/UCB-based algorithms.
