  In this paper, we explore a multi-agent reinforcement learning approach to
address the design problem of communication and control strategies for
multi-agent cooperative transport. Typical end-to-end deep neural network
policies may be insufficient for covering communication and control; these
methods cannot decide the timing of communication and can only work with
fixed-rate communications. Therefore, our framework exploits event-triggered
architecture, namely, a feedback controller that computes the communication
input and a triggering mechanism that determines when the input has to be
updated again. Such event-triggered control policies are efficiently optimized
using a multi-agent deep deterministic policy gradient. We confirmed that our
approach could balance the transport performance and communication savings
through numerical simulations.
