  Communication enables agents to cooperate to achieve their goals. Learning
when to communicate, i.e., sparse (in time) communication, and whom to message
is particularly important when bandwidth is limited. Recent work in learning
sparse individualized communication, however, suffers from high variance during
training, where decreasing communication comes at the cost of decreased reward,
particularly in cooperative tasks. We use the information bottleneck to reframe
sparsity as a representation learning problem, which we show naturally enables
lossless sparse communication at lower budgets than prior art. In this paper,
we propose a method for true lossless sparsity in communication via Information
Maximizing Gated Sparse Multi-Agent Communication (IMGS-MAC). Our model uses
two individualized regularization objectives, an information maximization
autoencoder and sparse communication loss, to create informative and sparse
communication. We evaluate the learned communication `language' through direct
causal analysis of messages in non-sparse runs to determine the range of
lossless sparse budgets, which allow zero-shot sparsity, and the range of
sparse budgets that will inquire a reward loss, which is minimized by our
learned gating function with few-shot sparsity. To demonstrate the efficacy of
our results, we experiment in cooperative multi-agent tasks where communication
is essential for success. We evaluate our model with both continuous and
discrete messages. We focus our analysis on a variety of ablations to show the
effect of message representations, including their properties, and lossless
performance of our model.
