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Communication stands as a potent mechanism to harmonize the behaviors ofmultiple agents. However, existing works primarily concentrate on broadcastcommunication, which not only lacks practicality, but also leads to informationredundancy. This surplus, one-fits-all information could adversely impact thecommunication efficiency. Furthermore, existing works often resort to basicmechanisms to integrate observed and received information, impairing thelearning process. To tackle these difficulties, we propose Targeted and TrustedMulti-Agent Communication (T2MAC), a straightforward yet effective method thatenables 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 sourcesat an evidence level. This process enables agents to collectively use evidencegarnered from multiple perspectives, fostering trusted and cooperativebehaviors. We evaluate our method on a diverse set of cooperative multi-agenttasks, with varying difficulties, involving different scales and ranging fromHallway, MPE to SMAC. The experiments indicate that the proposed model not onlysurpasses the state-of-the-art methods in terms of cooperative performance andcommunication efficiency, but also exhibits impressive generalization.

T2MAC: Targeted and Trusted Multi-Agent Communication through Selective Engagement and Evidence-Driven Integration