  Communication stands as a potent mechanism to harmonize the behaviors of
multiple agents. However, existing works primarily concentrate on broadcast
communication, which not only lacks practicality, but also leads to information
redundancy. This surplus, one-fits-all information could adversely impact the
communication efficiency. Furthermore, existing works often resort to basic
mechanisms to integrate observed and received information, impairing the
learning process. To tackle these difficulties, we propose Targeted and Trusted
Multi-Agent Communication (T2MAC), a straightforward yet effective method that
enables agents to learn selective engagement and evidence-driven integration.
With T2MAC, agents have the capability to craft individualized messages,
pinpoint ideal communication windows, and engage with reliable partners,
thereby refining communication efficiency. Following the reception of messages,
the agents integrate information observed and received from different sources
at an evidence level. This process enables agents to collectively use evidence
garnered from multiple perspectives, fostering trusted and cooperative
behaviors. We evaluate our method on a diverse set of cooperative multi-agent
tasks, with varying difficulties, involving different scales and ranging from
Hallway, MPE to SMAC. The experiments indicate that the proposed model not only
surpasses the state-of-the-art methods in terms of cooperative performance and
communication efficiency, but also exhibits impressive generalization.
