Mean-variance and value at risk in multi-armed bandit problems
Mean-variance and value at risk in multi-armed bandit problems
复制标题
多臂老虎机问题中的均值方差和风险值
DOI：
10.1109/allerton.2015.7447162
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发表时间：
2015
期刊：
影响因子：
--
通讯作者：
Qing Zhao
中科院分区：
文献类型：
--
作者：
Sattar Vakili;Qing Zhao
研究了不同风险度量下的风险规避型多武装盗匪问题。我们考虑了三种风险缓解模型。在第一个模型中，不同时间获得的奖励值的变化被视为风险，目标是最小化观察到的奖励的均值方差。在第二个和第三个模型中，利息的数量是在时间范围结束时的总回报，目标分别是最小化平均方差和最大化总回报的风险值。我们制定了规避风险的在线学习政策，并分析了它们的后悔表现。我们还在观测的均值-方差模型下提供了遗憾的严格下界。
We study risk-averse multi-armed bandit problems under different risk measures. We consider three risk mitigation models. In the first model, the variations in the reward values obtained at different times are considered as risk and the objective is to minimize the mean-variance of the observed rewards. In the second and the third models, the quantity of interest is the total reward at the end of the time horizon, and the objective is to minimize the mean-variance and maximize the value at risk of the total reward, respectively. We develop risk-averse online learning policies and analyze their regret performance. We also provide tight lower bounds on regret under the model of mean-variance of observations.