Finite state multi-armed bandit problems: Sensitive-discount, average-reward and average-overtaking optimality

We express Gittins indices for multi-armed bandit problems as Laurent expansions around discount factor 1. The coefficients of these expansions are then used to characterize stationary optimal policies when the optimality criteria are sensitive-discount optimality (otherwise known as Blackwell optimality), average-reward optimality and average-overtaking optimality. We also obtain bounds and derive optimality conditions for policies of a type that continue playing the same bandit as long as the state of that bandit remains in prescribed sets.