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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. In contrast toprevious works that propose using learning solutions for unlabelled motionplanning with constraints, we are able to demonstrate the scalability of ourmethods for a large number of robots. The curse of dimensionality oneencounters when working with a large number of robots is mitigated by employinga graph convolutional neural (GCN) network to parametrize policies for therobots. The GCN reduces the dimensionality of the problem by learning filtersthat aggregate information among robots locally, similar to how a convolutionalneural network is able to learn local features in an image. Additionally, byemploying a GCN 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.

Graph Policy Gradients for Large Scale Unlabeled Motion Planning with Constraints