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Deep reinforcement learning of event-triggered communication and control for multi-agent cooperative transport

2021-03-29 01:16:12
Kazuki Shibata, Tomohiko Jimbo, Takamitsu Matsubara
arXiv_RO
     
Abstract

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. Therefore, our framework exploits event-triggered architecture, namely, a feedback controller that computes the communication input and a triggering mechanism that determines when the input has to be updated again. Such event-triggered control policies are efficiently optimized using a multi-agent deep deterministic policy gradient. We confirmed that our approach could balance the transport performance and communication savings through numerical simulations.

Abstract (translated)

URL

https://arxiv.org/abs/2103.15260

PDF

https://arxiv.org/pdf/2103.15260.pdf

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