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.
StreamingBandit; Experimenting with Bandit Policies
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
abstract
A large number of statistical decision problems in the social sciences and beyond can be framed as a (contextual) multi-armed bandit problem. However, it is notoriously hard to develop and evaluate policies that tackle these types of problem, and to use such policies in applied studies. To address this issue, this paper introduces StreamingBandit, a Python web application for developing and testing bandit policies in field studies. StreamingBandit can sequentially select treatments using (online) policies in real time. Once StreamingBandit is implemented in an applied context, different policies can be tested, altered, nested, and compared. StreamingBandit makes it easy to apply a multitude of bandit policies for sequential allocation in field experiments, and allows for the quick development and re-use of novel policies. In this article, we detail the implementation logic of StreamingBandit and provide several examples of its use.
fields
cs.LG 1years
2019 1verdicts
CONDITIONAL 1representative citing papers
citing papers explorer
-
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.