Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: On the Identification and Mitigation of Weaknesses in the Knowledge
  Gradient Policy for Multi-Armed Bandits   The Knowledge Gradient (KG) policy was originally proposed for online ranking
and selection problems but has recently been adapted for use in online decision
making in general and multi-armed bandit problems (MABs) in particular. We
study its use in a class of exponential family MABs and identify weaknesses,
including a propensity to take actions which are dominated with respect to both
exploitation and exploration. We propose variants of KG which avoid such
errors. These new policies include an index heuristic which deploys a KG
approach to develop an approximation to the Gittins index. A numerical study
shows this policy to perform well over a range of MABs including those for
which index policies are not optimal. While KG does not make dominated actions
when bandits are Gaussian, it fails to be index consistent and appears not to
enjoy a performance advantage over competitor policies when arms are correlated
to compensate for its greater computational demands.

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The categories of this paper are: nucl-ex
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The categories of this paper are: dg-ga
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The categories of this paper are: hep-lat
9
The categories of this paper are: nlin
3
The categories of this paper are: physics
23
The categories of this paper are: solv-int
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The categories of this paper are: comp-gas
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The categories of this paper are: patt-sol
8
The categories of this paper are: hep-ex
13
The categories of this paper are: quant-ph
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The categories of this paper are: alg-geom
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The categories of this paper are: q-bio
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The categories of this paper are: q-fin
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The categories of this paper are: eess
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The categories of this paper are: econ
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The categories of this paper are: supr-con