Multi-Objective Optimization of the Textile Manufacturing Process Using Deep-Q-Network Based Multi-Agent Reinforcement Learning
This work aims to improve the efficiency and optimality of textile manufacturing processes for the textile industry by reducing the need for human intervention and handling increased complexity, representing an incremental improvement over traditional methods.
This paper addresses the multi-objective optimization of the textile manufacturing process, which is becoming increasingly complex. The authors propose a multi-agent reinforcement learning (MARL) framework using deep Q-networks to transform the optimization into a stochastic game, achieving optimal solutions for the textile ozonation process that outperform traditional approaches.
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.