  Over the last few years, the DRL paradigm has been widely adopted for 5G and
beyond network optimization because of its extreme adaptability to many
different scenarios. However, collecting and processing learning data entail a
significant cost in terms of communication and computational resources, which
is often disregarded in the networking literature. In this work, we analyze the
cost of learning in a resource-constrained system, defining an optimization
problem in which training a DRL agent makes it possible to improve the resource
allocation strategy but also reduces the number of available resources. Our
simulation results show that the cost of learning can be critical when
evaluating DRL schemes on the network edge and that assuming a cost-free
learning model can lead to significantly overestimating performance.
