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Learning Agent Communication under Limited Bandwidth by Message Pruning
[article]
2019
arXiv
pre-print
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 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
arXiv:1912.05304v1
fatcat:4dqvnrmfb5gdjlmciwgwi7n3fi