  In this paper, we present a learning method to solve the unlabelled motion
problem with motion constraints and space constraints in 2D space for a large
number of robots. To solve the problem of arbitrary dynamics and constraints we
propose formulating the problem as a multi-agent problem. We are able to
demonstrate the scalability of our methods for a large number of robots by
employing a graph neural network (GNN) to parameterize policies for the robots.
The GNN reduces the dimensionality of the problem by learning filters that
aggregate information among robots locally, similar to how a convolutional
neural network is able to learn local features in an image. Additionally, by
employing a GNN we are also able to overcome the computational overhead of
training policies for a large number of robots by first training graph filters
for a small number of robots followed by zero-shot policy transfer to a larger
number of robots. We demonstrate the effectiveness of our framework through
various simulations.
