Solving combinatorial problems represents an everyday challenge for vital problems such as logistics, resource allocation and optimization. In those contexts, machine learning, with its Reinforcement Learning and Graph Neural Networks branches, promises automation: give the machine input data and an evaluator to guide its learning process and, and the end of training, it will return feasible and high-quality outputs. Unfortunately, exploiting this potential is not an easy matter: in practice, the research so far has only focused on tailored solutions, small environments or, with changing fortunes, on specific sub-classes of combinatorial problems such as the TSP and its variants. The aim of this research is therefore assessing how and whether the latest deep learning findings in those fields also transfer to other practical problems.