Learning Agent Communication under Limited Bandwidth by Message Pruning
This addresses a practical limitation for multi-agent systems using deep reinforcement learning, but it is incremental as it builds on existing methods.
The paper tackles the problem of limited bandwidth in multi-agent communication by proposing a gating mechanism to prune less beneficial messages, resulting in significant message reduction with little performance impact and sometimes performance improvement.
Communication is a crucial factor for the big multi-agent world to stay organized and productive. Recently, Deep Reinforcement Learning (DRL) has been applied to learn the communication strategy and the control policy for multiple agents. However, the practical \emph{\textbf{limited bandwidth}} in multi-agent communication has been largely ignored by the existing DRL methods. Specifically, many methods keep sending messages incessantly, which consumes too much bandwidth. As a result, they are inapplicable to multi-agent systems with limited bandwidth. To handle this problem, we propose a gating mechanism to adaptively prune less beneficial messages. We evaluate the gating mechanism on several tasks. Experiments demonstrate that it can prune a lot of messages with little impact on performance. In fact, the performance may be greatly improved by pruning redundant messages. Moreover, the proposed gating mechanism is applicable to several previous methods, equipping them the ability to address bandwidth restricted settings.