Resourceful Contextual Bandits

Abstract

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 handles constrained resources other than time, and improves over a trivial reduction to the non-contextual case. We consider very general settings for both contextual bandits (arbitrary policy sets, e.g. Dudik et al. (UAI'11)) and bandits with resource constraints (bandits with knapsacks, Badanidiyuru et al. (FOCS'13)), and prove a regret guarantee with near-optimal statistical properties.

This is the full version of a paper in COLT 2014. Version history: (v2) Added some details to one of the proofs, (v3) a big revision following comments from COLT reviewers (but no new results), (v4) edits in related work, minor edits elsewhere. (v6) A correction for Theorem 3, corollary for contextual dynamic pricing with discretization; updated follow-up work & open questions

References in corpus (5)

• Taming the Monster: A Fast and Simple Algorithm for Contextual Bandits
• An efficient algorithm for contextual bandits with knapsacks, and an extension to concave objectives
• Multi-Armed Bandits in Metric Spaces
• Bandits with concave rewards and convex knapsacks
• Approximation Algorithms for Correlated Knapsacks and Non-Martingale Bandits

Cited by in corpus (9)

• Algorithms with Logarithmic or Sublinear Regret for Constrained Contextual Bandits
• Stochastic Contextual Bandits with Known Reward Functions
• Online Allocation and Pricing: Constant Regret via Bellman Inequalities
• Adaptive Contract Design for Crowdsourcing Markets: Bandit Algorithms for Repeated Principal-Agent Problems
• Dynamic Pricing with Demand Covariates
• Risk-Aware Continuous Control with Neural Contextual Bandits
• Matching while Learning
• Risk-Aware Algorithms for Adversarial Contextual Bandits
• Price of Safety in Linear Best Arm Identification