Deep reinforcement learning of event-triggered communication and control for multi-agent cooperative transport
Kazuki Shibata Tomohiko Jimbo Takamitsu Matsubara Kazuki Shibata Tomohiko Jimbo Takamitsu Matsubara
In this paper, we explore a multi-agent reinforcement learning approach to address the design problem of communication and control strategies for multi-agent cooperative transport. Typical end-to-end deep neural network policies may be insufficient for covering communication and control; these methods cannot decide the timing of communication and can only work with fixed-rate communications. There...