  Wireless systems resource allocation refers to perpetual and challenging
nonconvex constrained optimization tasks, which are especially timely in modern
communications and networking setups involving multiple users with
heterogeneous objectives and imprecise or even unknown models and/or channel
statistics. In this paper, we propose a technically grounded and scalable
primal-dual deterministic policy gradient method for efficiently learning
optimal parameterized resource allocation policies. Our method not only
efficiently exploits gradient availability of popular universal policy
representations, such as deep neural networks, but is also truly model-free, as
it relies on consistent zeroth-order gradient approximations of the associated
random network services constructed via low-dimensional perturbations in action
space, thus fully bypassing any dependence on critics. Both theory and
numerical simulations confirm the efficacy and applicability of the proposed
approach, as well as its superiority over the current state of the art in terms
of both achieving near-optimal performance and scalability.
