1 research outputs found
Memory-Constrained No-Regret Learning in Adversarial Bandits
An adversarial bandit problem with memory constraints is studied where only
the statistics of a subset of arms can be stored. A hierarchical learning
policy that requires only a sublinear order of memory space in terms of the
number of arms is developed. Its sublinear regret orders with respect to the
time horizon are established for both weak regret and shifting regret. This
work appears to be the first on memory-constrained bandit problems under the
adversarial setting.Comment: Accepted by IEEE Transactions on Signal Processin