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

03/29/2021
by   Kazuki Shibata, et al.
0

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.

READ FULL TEXT

page 1

page 6

research
12/05/2022

Deep reinforcement learning of event-triggered communication and consensus-based control for distributed cooperative transport

In this paper, we present a solution to a design problem of control stra...
research
10/10/2020

Event-Triggered Multi-agent Reinforcement Learning with Communication under Limited-bandwidth Constraint

Communicating with each other in a distributed manner and behaving as a ...
research
12/06/2022

Learning Locally, Communicating Globally: Reinforcement Learning of Multi-robot Task Allocation for Cooperative Transport

We consider task allocation for multi-object transport using a multi-rob...
research
04/06/2020

Multi-Agent Deep Stochastic Policy Gradient for Event Based Dynamic Spectrum Access

We consider the dynamic spectrum access (DSA) problem where K Internet o...
research
05/01/2020

Smart Containers With Bidding Capacity: A Policy Gradient Algorithm for Semi-Cooperative Learning

Smart modular freight containers – as propagated in the Physical Interne...
research
02/28/2023

Multi-Agent Reinforcement Learning for Pragmatic Communication and Control

The automation of factories and manufacturing processes has been acceler...
research
09/10/2019

Energy Conscious Over-actuated Multi-Agent Payload Transport Robot: Simulations and Preliminary Physical Validation

In this work, we consider a multi-wheeled payload transport system. Each...

Please sign up or login with your details

Forgot password? Click here to reset