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

翻译：多智能体系统中的信息交换能够提升智能体间的协作能力，尤其是在部分可观测环境中。现实世界中的通信往往通过不完美信道进行，这要求智能体应对潜在信息丢失带来的不确定性。本文研究一种合作式多智能体系统，其中智能体通过有限且不可靠的信道以去中心化方式执行动作并交换信息。为应对信道约束，我们提出一种基于独立Q学习的新型通信方法。该方法允许智能体根据局部观测和信道特性动态调整信息共享量，通过发送不同长度的消息实现。除消息长度选择外，智能体还学习编码与解码消息以优化联合训练策略。实验表明，在新型协作数字预测环境中，我们的方法优于不具备自适应能力的方案，并在交通枢纽环境中讨论了其局限性。