  Logistics optimization nowadays is becoming one of the hottest areas in the
AI community. In the past year, significant advancements in the domain were
achieved by representing the problem in a form of graph. Another promising area
of research was to apply reinforcement learning algorithms to the above task.
In our work, we made advantage of using both approaches and apply reinforcement
learning on a graph. To do that, we have analyzed the most recent results in
both fields and selected SOTA algorithms both from graph neural networks and
reinforcement learning. Then, we combined selected models on the problem of
AMOD systems optimization for the transportation network of New York city. Our
team compared three algorithms - GAT, Pro-CNN and PTDNet - to bring to the fore
the important nodes on a graph representation. Finally, we achieved SOTA
results on AMOD systems optimization problem employing PTDNet with GNN and
training them in reinforcement fashion.
  Keywords: Graph Neural Network (GNN), Logistics optimization, Reinforcement
Learning
