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An Online Learning Approach to Optimizing Time-Varying Costs of AoI
[article]
2021
arXiv
pre-print
We consider systems that require timely monitoring of sources over a communication network, where the cost of delayed information is unknown, time-varying and possibly adversarial. For the single source monitoring problem, we design algorithms that achieve sublinear regret compared to the best fixed policy in hindsight. For the multiple source scheduling problem, we design a new online learning algorithm called Follow-the-Perturbed-Whittle-Leader and show that it has low regret compared to the
arXiv:2105.13383v1
fatcat:5izsh7s5ibf7zbjn2viah6bcoe