Projective cone scheduling defines a large class of rate-stabilizing policies
for queueing models relevant to several applications. While there exists
considerable theory on the properties of projective cone schedulers, there is
little practical guidance on choosing the parameters that define them. In this
paper, we propose an algorithm for designing an automated projective cone
scheduling system based on observations of an expert projective cone scheduler.
We show that the estimated scheduling policy is able to emulate the expert in
the sense that the average loss realized by the learned policy will converge to
zero. Specifically, for a system with $n$ queues observed over a time horizon
$T$, the average loss for the algorithm is $O(\ln(T)\sqrt{\ln(n)/T})$. This
upper bound holds regardless of the statistical characteristics of the system.
The algorithm uses the multiplicative weights update method and can be applied
online so that additional observations of the expert scheduler can be used to
improve an existing estimate of the policy. This provides a data-driven method
for designing a scheduling policy based on observations of a human expert. We
demonstrate the efficacy of the algorithm with a simple numerical example and
discuss several extensions.