2017/04/23 by Jacob Andreas, Anca D. Dragan, Andreas, Jacob +3 · 1 citation
Computer Science · #Adversarial Robustness in Machine Learning #Computation and Language (cs.CL) #FOS: Computer and information sciences #Multimodal Machine Learning Applications #Neural and Evolutionary Computing (cs.NE) #Reinforcement Learning in Robotics
paper · pdf · doi:10.48550/arxiv.1704.06960
openalex publication_date 2017/04/23 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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 the insight that agent messages and natural language strings mean the same thing if they induce the same belief about the world in a listener. We present theoretical guarantees and empirical evidence that our approach preserves both the semantics and pragmatics of messages by ensuring that players communicating through a translation layer do not suffer a substantial loss in reward relative to players with a common language.