  Many tasks in AI require the collaboration of multiple agents. Typically, the
communication protocol between agents is manually specified and not altered
during training. In this paper we explore a simple neural model, called
CommNet, that uses continuous communication for fully cooperative tasks. The
model consists of multiple agents and the communication between them is learned
alongside their policy. We apply this model to a diverse set of tasks,
demonstrating the ability of the agents to learn to communicate amongst
themselves, yielding improved performance over non-communicative agents and
baselines. In some cases, it is possible to interpret the language devised by
the agents, revealing simple but effective strategies for solving the task at
hand.
