Multi-Agent Reinforcement Learning for Real-Time Dynamic Production Scheduling in a Robot Assembly Cell-Reference-Cited by-同舟云学术

Multi-Agent Reinforcement Learning for Real-Time Dynamic Production Scheduling in a Robot Assembly Cell

Published:2022-07 Issue:3 Volume:7 Page:7684-7691
ISSN:2377-3766
Container-title:IEEE Robotics and Automation Letters
language:
Short-container-title:IEEE Robot. Autom. Lett.

Author:

Johnson Dazzle¹^ORCID,Chen Gang²^ORCID,Lu Yuqian¹^ORCID

Affiliation:

1. Department of Mechanical and Mechatronics Engineering, The University of Auckland, Auckland, New Zealand

2. School of Engineering and Computer Science, Victoria University of Wellington, Wellington, New Zealand

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Artificial Intelligence,Control and Optimization,Computer Science Applications,Computer Vision and Pattern Recognition,Mechanical Engineering,Human-Computer Interaction,Biomedical Engineering,Control and Systems Engineering

Link

http://xplorestaging.ieee.org/ielx7/7083369/9750005/09801608.pdf?arnumber=9801608

Reference24 articles.

1. Learning Scheduling Policies for Multi-Robot Coordination With Graph Attention Networks

2. Heterogeneous graph attention networks for scalable multi-robot scheduling with temporospatial constraints

3. A comprehensive survey of multiagent reinforcement learning;bu?oniu;IEEE Trans Syst Man Cybern Part C Appl Rev,2008

4. Real-Time Scheduling for Dynamic Partial-No-Wait Multiobjective Flexible Job Shop by Deep Reinforcement Learning

5. Smart Manufacturing Scheduling With Edge Computing Using Multiclass Deep Q Network

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