  While it is known that communication facilitates cooperation in multi-agent
settings, it is unclear how to design artificial agents that can learn to
effectively and efficiently communicate with each other. Much research on
communication emergence uses reinforcement learning (RL) and explores
unsituated communication in one-step referential tasks -- the tasks are not
temporally interactive and lack time pressures typically present in natural
communication. In these settings, agents may successfully learn to communicate,
but they do not learn to exchange information concisely -- they tend towards
over-communication and an inefficient encoding. Here, we explore situated
communication in a multi-step task, where the acting agent has to forgo an
environmental action to communicate. Thus, we impose an opportunity cost on
communication and mimic the real-world pressure of passing time. We compare
communication emergence under this pressure against learning to communicate
with a cost on articulation effort, implemented as a per-message penalty (fixed
and progressively increasing). We find that while all tested pressures can
disincentivise over-communication, situated communication does it most
effectively and, unlike the cost on effort, does not negatively impact
emergence. Implementing an opportunity cost on communication in a temporally
extended environment is a step towards embodiment, and might be a pre-condition
for incentivising efficient, human-like communication.
