  In artificial multi-agent systems, the ability to learn collaborative
policies is predicated upon the agents' communication skills: they must be able
to encode the information received from the environment and learn how to share
it with other agents as required by the task at hand. We present a deep
reinforcement learning approach, Connectivity Driven Communication (CDC), that
facilitates the emergence of multi-agent collaborative behaviour only through
experience. The agents are modelled as nodes of a weighted graph whose
state-dependent edges encode pair-wise messages that can be exchanged. We
introduce a graph-dependent attention mechanisms that controls how the agents'
incoming messages are weighted. This mechanism takes into full account the
current state of the system as represented by the graph, and builds upon a
diffusion process that captures how the information flows on the graph. The
graph topology is not assumed to be known a priori, but depends dynamically on
the agents' observations, and is learnt concurrently with the attention
mechanism and policy in an end-to-end fashion. Our empirical results show that
CDC is able to learn effective collaborative policies and can over-perform
competing learning algorithms on cooperative navigation tasks.
