The Knowledge Gradient (KG) policy was originally proposed for offline ranking and selection problems but has recently been adapted for use in online decisionmaking 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 take 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. arXiv:1607.05970v2 [stat.ML]
Recently, REF identify weaknesses of the knowledge gradient policy for online decision making and propose variants of the policy to overcome the weaknesses.
On the Identification and Mitigation of Weaknesses in the Knowledge Gradient Policy for Multi-Armed Bandits
Probability in the Engineering and Informational Sciences, 31(2) 239-263 (2017)
Mathematics Computer Science