Multi-Objective Optimization of the Textile Manufacturing Process Using Deep-Q-Network Based Multi-Agent Reinforcement Learning

12/02/2020
by   Zhenglei He, et al.
0

Multi-objective optimization of the textile manufacturing process is an increasing challenge because of the growing complexity involved in the development of the textile industry. The use of intelligent techniques has been often discussed in this domain, although a significant improvement from certain successful applications has been reported, the traditional methods failed to work with high-as well as human intervention. Upon which, this paper proposed a multi-agent reinforcement learning (MARL) framework to transform the optimization process into a stochastic game and introduced the deep Q-networks algorithm to train the multiple agents. A utilitarian selection mechanism was employed in the stochastic game, which (-greedy policy) in each state to avoid the interruption of multiple equilibria and achieve the correlated equilibrium optimal solutions of the optimizing process. The case study result reflects that the proposed MARL system is possible to achieve the optimal solutions for the textile ozonation process and it performs better than the traditional approaches.

READ FULL TEXT
POST COMMENT

Comments

There are no comments yet.

Authors

page 15

12/29/2020

A Deep Reinforcement Learning Based Multi-Criteria Decision Support System for Textile Manufacturing Process Optimization

Textile manufacturing is a typical traditional industry involving high c...
11/14/2020

Opponent Learning Awareness and Modelling in Multi-Objective Normal Form Games

Many real-world multi-agent interactions consider multiple distinct crit...
03/14/2022

Efficient Model-based Multi-agent Reinforcement Learning via Optimistic Equilibrium Computation

We consider model-based multi-agent reinforcement learning, where the en...
06/08/2021

Multi-Agent Cooperative Bidding Games for Multi-Objective Optimization in e-Commercial Sponsored Search

Bid optimization for online advertising from single advertiser's perspec...
01/19/2021

Dynamic Bicycle Dispatching of Dockless Public Bicycle-sharing Systems using Multi-objective Reinforcement Learning

As a new generation of Public Bicycle-sharing Systems (PBS), the dockles...
10/10/2021

Multi-condition multi-objective optimization using deep reinforcement learning

A multi-condition multi-objective optimization method that can find Pare...
03/02/2019

A Cooperative Multi-Agent Reinforcement Learning Framework for Resource Balancing in Complex Logistics Network

Resource balancing within complex transportation networks is one of the ...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.