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Deep Reinforcement Learning of Event-triggered Communication and Consensus-based Control for Distributed Cooperative Transport by Shibata, Kazuki; Jimbo, Tomohiko; Matsubara, Takamitsu is a scholarly article available to read on EtoBox.

What is Deep Reinforcement Learning of Event-triggered Communication and Consensus-based Control for Distributed Cooperative Transport about?

In this paper, we present a solution to a design problem of control strategies for multi-agent cooperative transport. Although existing learning-based methods assume that the number of agents is the same as that in the training environment, the number might differ in reality considering that the robots' batteries may completely discharge, or additional robots may be introduced to reduce the time required to complete a task. Therefore, it is crucial that the learned strategy be applicable to scenarios wherein the number of agents differs from that in the training environment. In this paper, we propose a novel multi-agent reinforcement learning framework of event-triggered communication and consensus-based control for distributed cooperative transport. The proposed policy model estimates the resultant force and torque in a consensus manner using the estimates of the resultant force and torque with the neighborhood agents. Moreover, it computes the control and communication inputs to determine when to communicate with the neighboring agents under local observations and estimates of the resultant force and torque. Therefore, the proposed framework can balance the control performance an

Author

Shibata, Kazuki; Jimbo, Tomohiko; Matsubara, Takamitsu

Published

2022

Language

EN