Latent Bandits Revisited

Understand

A latent bandit problem is one in which the learning agent knows the arm reward distributions conditioned on an unknown discrete latent state.

• The primary goal of the agent is to identify the latent state, after which it can act optimally.
• This setting is a natural midpoint between online and offline learning---complex models can be learned offline with the agent identifying latent state online---of practical relevance in, say, recommender systems.
• In this work, we propose general algorithms for this setting, based on both upper confidence bounds (UCBs) and Thompson sampling.