  Collaborative decision making in multi-agent systems typically requires a
predefined communication protocol among agents. Usually, agent-level
observations are locally processed and information is exchanged using the
predefined protocol, enabling the team to perform more efficiently than each
agent operating in isolation. In this work, we consider the situation where
agents, with complementary sensing modalities must co-operate to achieve a
common goal/task by learning an efficient communication protocol. We frame the
problem within an actor-critic scheme, where the agents learn optimal policies
in a centralized fashion, while taking action in a distributed manner. We
provide an interpretation of the emergent communication between the agents. We
observe that the information exchanged is not just an encoding of the raw
sensor data but is, rather, a specific set of directive actions that depend on
the overall task. Simulation results demonstrate the interpretability of the
learnt communication in a variety of tasks.
