Resourceful Contextual Bandits
Proceedings of The 27th Conference on Learning Theory, PMLR 35:1109-1134, 2014.
We study contextual bandits with ancillary constraints on resources, which are common in real-world applications such as choosing ads or dynamic pricing of items. We design the first algorithm for solving these problems that improves over a trivial reduction to the non-contextual case. We consider very general settings for both contextual bandits (arbitrary policy sets, Dudik et al. (2011)) and bandits with resource constraints (bandits with knapsacks, Badanidiyuru et al. (2013a)), and prove a regret guarantee with near-optimal statistical properties.