  We propose a targeted communication architecture for multi-agent
reinforcement learning, where agents learn both what messages to send and whom
to address them to while performing cooperative tasks in partially-observable
environments. This targeting behavior is learnt solely from downstream
task-specific reward without any communication supervision. We additionally
augment this with a multi-round communication approach where agents coordinate
via multiple rounds of communication before taking actions in the environment.
We evaluate our approach on a diverse set of cooperative multi-agent tasks, of
varying difficulties, with varying number of agents, in a variety of
environments ranging from 2D grid layouts of shapes and simulated traffic
junctions to 3D indoor environments, and demonstrate the benefits of targeted
and multi-round communication. Moreover, we show that the targeted
communication strategies learned by agents are interpretable and intuitive.
Finally, we show that our architecture can be easily extended to mixed and
competitive environments, leading to improved performance and sample complexity
over recent state-of-the-art approaches.
