  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.
