Learning Agent Communication under Limited Bandwidth by Message Pruning

Understand

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.