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We study the adversarial multi-armed bandit problem where partialobservations are available and where, in addition to the loss incurred for eachaction, a \emph{switching cost} is incurred for shifting to a new action. Allpreviously known results incur a factor proportional to the independence numberof the feedback graph. We give a new algorithm whose regret guarantee dependsonly on the domination number of the graph. We further supplement that resultwith a lower bound. Finally, we also give a new algorithm with improved policyregret bounds when partial counterfactual feedback is available.

Bandits with Feedback Graphs and Switching Costs