Liquid-Graph Time-Constant Network for Multi-Agent Systems Control
Abstract
In this paper, we propose the Liquid-Graph Time-
constant (LGTC) network, a continuous graph neural network
(GNN) model for control of multi-agent systems based on the
recent Liquid Time Constant (LTC) network. We analyse its
stability leveraging contraction analysis and propose a closed-
form model that preserves the model contraction rate and does
not require solving an ODE at each iteration. Compared to
discrete models like Graph Gated Neural Networks (GGNNs),
the higher expressivity of the proposed model guarantees
remarkable performance while reducing the large amount
of communicated variables normally required by GNNs. We
evaluate our model on a distributed multi-agent control case
study (flocking) taking into account variable communication
range and scalability under non-instantaneous communication
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