  In this paper, we present a perception-action-communication loop design using
Vision-based Graph Aggregation and Inference (VGAI). This multi-agent
decentralized learning-to-control framework maps raw visual observations to
agent actions, aided by local communication among neighboring agents. Our
framework is implemented by a cascade of a convolutional and a graph neural
network (CNN / GNN), addressing agent-level visual perception and feature
learning, as well as swarm-level communication, local information aggregation
and agent action inference, respectively. By jointly training the CNN and GNN,
image features and communication messages are learned in conjunction to better
address the specific task. We use imitation learning to train the VGAI
controller in an offline phase, relying on a centralized expert controller.
This results in a learned VGAI controller that can be deployed in a distributed
manner for online execution. Additionally, the controller exhibits good scaling
properties, with training in smaller teams and application in larger teams.
Through a multi-agent flocking application, we demonstrate that VGAI yields
performance comparable to or better than other decentralized controllers, using
only the visual input modality and without accessing precise location or motion
state information.
