  We tackle the problem of joint frequency and power allocation while
emphasizing the generalization capability of a deep reinforcement learning
model. Most of the existing methods solve reinforcement learning-based wireless
problems for a specific pre-determined wireless network scenario. The
performance of a trained agent tends to be very specific to the network and
deteriorates when used in a different network operating scenario (e.g.,
different in size, neighborhood, and mobility, among others). We demonstrate
our approach to enhance training to enable a higher generalization capability
during inference of the deployed model in a distributed multi-agent setting in
a hostile jamming environment. With all these, we show the improved training
and inference performance of the proposed methods when tested on previously
unseen simulated wireless networks of different sizes and architectures. More
importantly, to prove practical impact, the end-to-end solution was implemented
on the embedded software-defined radio and validated using over-the-air
evaluation.
