  The field of emergent communication aims to understand the characteristics of
communication as it emerges from artificial agents solving tasks that require
information exchange. Communication with discrete messages is considered a
desired characteristic, for both scientific and applied reasons. However,
training a multi-agent system with discrete communication is not
straightforward, requiring either reinforcement learning algorithms or relaxing
the discreteness requirement via a continuous approximation such as the
Gumbel-softmax. Both these solutions result in poor performance compared to
fully continuous communication. In this work, we propose an alternative
approach to achieve discrete communication -- quantization of communicated
messages. Using message quantization allows us to train the model end-to-end,
achieving superior performance in multiple setups. Moreover, quantization is a
natural framework that runs the gamut from continuous to discrete
communication. Thus, it sets the ground for a broader view of multi-agent
communication in the deep learning era.
