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Several approaches have recently been proposed for learning decentralized deep multiagent policies that coordinate via a differentiable communication channel. While these policies are effective for many tasks, interpretation of their induced communication strategies has remained a challenge. Here we propose to interpret agents' messages by translating them. Unlike in typical machine translation problems, we have no parallel data to learn from. Instead we develop a translation model based on thedoi:10.18653/v1/p17-1022 dblp:conf/acl/AndreasDK17 fatcat:ybmtxlxiffavjjd3zciinrkvba