Communication is an important factor for the big multi-agent world to stay
organized and productive. Recently, the AI community has applied the Deep
Reinforcement Learning (DRL) to learn the communication strategy and the
control policy for multiple agents. However, when implementing the
communication for real-world multi-agent applications, there is a more
practical limited-bandwidth restriction, which has been largely ignored by the
existing DRL-based methods. Specifically, agents trained by most previous
methods keep sending messages incessantly in every control cycle; due to
emitting too many messages, these methods are unsuitable to be applied to the
real-world systems that have a limited bandwidth to transmit the messages. To
handle this problem, we propose a gating mechanism to adaptively prune
unprofitable messages. Results show that the gating mechanism can prune more
than 80% messages with little damage to the performance. Moreover, our method
outperforms several state-of-the-art DRL-based and rule-based methods by a
large margin in both the real-world packet routing tasks and four benchmark
tasks.

本研究提出了一种门控机制来自适应地剪枝无用消息，使得在多智能体通信时，性能得到显著提升，同时在真实世界中的数据包路由任务和四个基准测试任务中，比现有的基于深度强化学习和基于规则的方法都表现良好。