A reinforcement learning based decision support system in textile manufacturing process
This is an incremental application of reinforcement learning to support decision-making in textile manufacturing, a domain-specific problem.
The paper tackled optimizing color fading ozonation in textile manufacturing by formulating it as a Markov Decision Process and using Q-learning, finding that the model effectively expressed the problem and reinforcement learning is applicable with promising prospects.
This paper introduced a reinforcement learning based decision support system in textile manufacturing process. A solution optimization problem of color fading ozonation is discussed and set up as a Markov Decision Process (MDP) in terms of tuple {S, A, P, R}. Q-learning is used to train an agent in the interaction with the setup environment by accumulating the reward R. According to the application result, it is found that the proposed MDP model has well expressed the optimization problem of textile manufacturing process discussed in this paper, therefore the use of reinforcement learning to support decision making in this sector is conducted and proven that is applicable with promising prospects.