  The information ratio offers an approach to assessing the efficacy with which
an agent balances between exploration and exploitation. Originally, this was
defined to be the ratio between squared expected regret and the mutual
information between the environment and action-observation pair, which
represents a measure of information gain. Recent work has inspired
consideration of alternative information measures, particularly for use in
analysis of bandit learning algorithms to arrive at tighter regret bounds. We
investigate whether quantification of information via such alternatives can
improve the realized performance of information-directed sampling, which aims
to minimize the information ratio.
