Memory-Constrained No-Regret Learning in Adversarial Bandits

Abstract

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.

Accepted by IEEE Transactions on Signal Processing

References in corpus (2)

• Beating Stochastic and Adversarial Semi-bandits Optimally and Simultaneously
• Multi-agent online learning in time-varying games