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Collaborative decision making in multi-agent systems typically requires apredefined communication protocol among agents. Usually, agent-levelobservations are locally processed and information is exchanged using thepredefined protocol, enabling the team to perform more efficiently than eachagent operating in isolation. In this work, we consider the situation whereagents, with complementary sensing modalities must co-operate to achieve acommon goal/task by learning an efficient communication protocol. We frame theproblem within an actor-critic scheme, where the agents learn optimal policiesin a centralized fashion, while taking action in a distributed manner. Weprovide an interpretation of the emergent communication between the agents. Weobserve that the information exchanged is not just an encoding of the rawsensor data but is, rather, a specific set of directive actions that depend onthe overall task. Simulation results demonstrate the interpretability of thelearnt communication in a variety of tasks.

A perspective on multi-agent communication for information fusion