We consider the problem of the limited-bandwidth communication for
multi-agent reinforcement learning, where agents cooperate with the assistance
of a communication protocol and a scheduler. The protocol and scheduler jointly
determine which agent is communicating what message and to whom. Under the
limited bandwidth constraint, a communication protocol is required to generate
informative messages. Meanwhile, an unnecessary communication connection should
not be established because it occupies limited resources in vain. In this
paper, we develop an Informative Multi-Agent Communication (IMAC) method to
learn efficient communication protocols as well as scheduling. First, from the
perspective of communication theory, we prove that the limited bandwidth
constraint requires low-entropy messages throughout the transmission. Then
inspired by the information bottleneck principle, we learn a valuable and
compact communication protocol and a weight-based scheduler. To demonstrate the
efficiency of our method, we conduct extensive experiments in various
cooperative and competitive multi-agent tasks with different numbers of agents
and different bandwidths. We show that IMAC converges faster and leads to
efficient communication among agents under the limited bandwidth as compared to
many baseline methods.

研究了在有限带宽传输的情况下，多智能体强化学习所需的有效通信协议和分配方法，并基于信息瓶颈原则学习了一种有价值的、紧凑的通信协议及其权重调度策略。通过实验，证明了这种方法在有限的带宽条件下实现了有效通信。