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Information exchange in multi-agent systems improves the cooperation amongagents, especially in partially observable settings. In the real world,communication is often carried out over imperfect channels. This requiresagents to handle uncertainty due to potential information loss. In this paper,we consider a cooperative multi-agent system where the agents act and exchangeinformation in a decentralized manner using a limited and unreliable channel.To cope with such channel constraints, we propose a novel communicationapproach based on independent Q-learning. Our method allows agents todynamically adapt how much information to share by sending messages ofdifferent sizes, depending on their local observations and the channel'sproperties. In addition to this message size selection, agents learn to encodeand decode messages to improve their jointly trained policies. We show that ourapproach outperforms approaches without adaptive capabilities in a novelcooperative digit-prediction environment and discuss its limitations in thetraffic junction environment.

Learning to Cooperate and Communicate Over Imperfect Channels