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

Large Scale Distributed Collaborative Unlabeled Motion Planning with Graph Policy Gradients