Abstract
This paper studies how and how much active experimentation is used in discounted or finite-horizon optimization problems with an agent who chooses actions sequentially from a finite set of actions, with rewards depending on unknown parameters associated with the actions. Closed-form approximations are developed for the optimal rules in these ‘multi-armed bandit’ problems. Some refinements and modifications of the basic structure of these approximations also provide a nearly optimal solution to the long-standing problem of incorporating switching costs into multi-armed bandits.
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