Motivated by applications in personalized web services and clinical research, we consider a multi-armed bandit problem in a setting where the mean reward of each arm is associated with some covariates. A multi-stage randomized allocation with arm elimination algorithm is proposed to combine the flexibility in reward function modeling and a theoretical guarantee of a cumulative regret minimax rate. When the function smoothness parameter is unknown, the algorithm is equipped with a histogram estimation based smoothness parameter selector using Lepski’s method, and is shown to maintain the regret minimax rate up to a logarithmic factor under a “self-similarity” condition.
展开▼
