  In this paper, we consider the problem of resource congestion control for
competing online learning agents. On the basis of non-cooperative game as the
model for the interaction between the agents, and the noisy online mirror
ascent as the model for rational behavior of the agents, we propose a novel
pricing mechanism which gives the agents incentives for sustainable use of the
resources. Our mechanism is distributed and resource-centric, in the sense that
it is done by the resources themselves and not by a centralized instance, and
that it is based rather on the congestion state of the resources than the
preferences of the agents. In case that the noise is persistent, and for
several choices of the intrinsic parameter of the agents, such as their
learning rate, and of the mechanism parameters, such as the learning rate of -,
the progressivity of the price-setters, and the extrinsic price sensitivity of
the agents, we show that the accumulative violation of the resource constraints
of the resulted iterates is sub-linear w.r.t. the time horizon. Moreover, we
provide numerical simulations to support our theoretical findings.
