Learning for Bandits under Action Erasures

arXiv:2406.18072v1 Announce Type: cross
Abstract: We consider a novel multi-arm bandit (MAB) setup, where a learner needs to communicate the actions to distributed agents over erasure channels, while the rewards for the actions are directly available to the learner through external sensors. In our model, while the distributed agents know if an action is erased, the central learner does not (there is no feedback), and thus does not know whether the observed reward resulted from the desired action or not. …

abstract action agents arm arxiv channels cs.lg distributed multi novel sensors setup stat.ml through type while