A δ-window extension of Li et al.'s offline bandit evaluation lets logged actions near a policy's choice count, giving a biased but rank-preserving (at coarse level) way to compare continuous-armed bandit policies.
Lock in Feedback in Sequential Experiments
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abstract
We often encounter situations in which an experimenter wants to find, by sequential experimentation, $x_{max} = \arg\max_{x} f(x)$, where $f(x)$ is a (possibly unknown) function of a well controllable variable $x$. Taking inspiration from physics and engineering, we have designed a new method to address this problem. In this paper, we first introduce the method in continuous time, and then present two algorithms for use in sequential experiments. Through a series of simulation studies, we show that the method is effective for finding maxima of unknown functions by experimentation, even when the maximum of the functions drifts or when the signal to noise ratio is low.
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cs.LG 1years
2019 1verdicts
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Exploring Offline Policy Evaluation for the Continuous-Armed Bandit Problem
A δ-window extension of Li et al.'s offline bandit evaluation lets logged actions near a policy's choice count, giving a biased but rank-preserving (at coarse level) way to compare continuous-armed bandit policies.