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

• URL: http://arxiv.org/abs/2103.15260v1
• Date: Mon, 29 Mar 2021 01:16:12 GMT
• Title: Deep reinforcement learning of event-triggered communication and control for multi-agent cooperative transport
• Authors: Kazuki Shibata, Tomohiko Jimbo and Takamitsu Matsubara
• Abstract summary: We explore a multi-agent reinforcement learning approach to address the design problem of communication and control strategies for cooperative transport. 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.
• Score: 9.891241465396098
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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