A reinforcement learning based decision support system in textile manufacturing process

• URL: http://arxiv.org/abs/2005.09867v1
• Date: Wed, 20 May 2020 06:33:47 GMT
• Title: A reinforcement learning based decision support system in textile manufacturing process
• Authors: Zhenglei He (GEMTEX), Kim Phuc Tran (GEMTEX), S\'ebastien Thomassey (GEMTEX), Xianyi Zeng (GEMTEX), Changhai Yi
• Abstract summary: This paper introduced a reinforcement learning support system in textile manufacturing process. It is found that the proposed MDP model has well expressed the optimization problem of textile manufacturing process.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• Enhancing Multi-Step Reasoning Abilities of Language Models through Direct Q-Function Optimization [50.485788083202124]
  Reinforcement Learning (RL) plays a crucial role in aligning large language models with human preferences and improving their ability to perform complex tasks. We introduce Direct Q-function Optimization (DQO), which formulates the response generation process as a Markov Decision Process (MDP) and utilizes the soft actor-critic (SAC) framework to optimize a Q-function directly parameterized by the language model. Experimental results on two math problem-solving datasets, GSM8K and MATH, demonstrate that DQO outperforms previous methods, establishing it as a promising offline reinforcement learning approach for aligning language models.
  arXiv  Detail & Related papers  (2024-10-11T23:29:20Z)
• Cognitive LLMs: Towards Integrating Cognitive Architectures and Large Language Models for Manufacturing Decision-making [51.737762570776006]
  LLM-ACTR is a novel neuro-symbolic architecture that provides human-aligned and versatile decision-making. Our framework extracts and embeds knowledge of ACT-R's internal decision-making process as latent neural representations. Our experiments on novel Design for Manufacturing tasks show both improved task performance as well as improved grounded decision-making capability.
  arXiv  Detail & Related papers  (2024-08-17T11:49:53Z)
• Optimized Task Assignment and Predictive Maintenance for Industrial Machines using Markov Decision Process [0.0]
  This paper considers a distributed decision-making approach for manufacturing task assignment and condition-based machine health maintenance. We propose the design of the decision-making agents based on Markov decision processes.
  arXiv  Detail & Related papers  (2024-01-20T12:12:14Z)
• Let's reward step by step: Step-Level reward model as the Navigators for Reasoning [64.27898739929734]
  Process-Supervised Reward Model (PRM) furnishes LLMs with step-by-step feedback during the training phase. We propose a greedy search algorithm that employs the step-level feedback from PRM to optimize the reasoning pathways explored by LLMs. To explore the versatility of our approach, we develop a novel method to automatically generate step-level reward dataset for coding tasks and observed similar improved performance in the code generation tasks.
  arXiv  Detail & Related papers  (2023-10-16T05:21:50Z)
• Flexible Job Shop Scheduling via Dual Attention Network Based Reinforcement Learning [73.19312285906891]
  In flexible job shop scheduling problem (FJSP), operations can be processed on multiple machines, leading to intricate relationships between operations and machines. Recent works have employed deep reinforcement learning (DRL) to learn priority dispatching rules (PDRs) for solving FJSP. This paper presents a novel end-to-end learning framework that weds the merits of self-attention models for deep feature extraction and DRL for scalable decision-making.
  arXiv  Detail & Related papers  (2023-05-09T01:35:48Z)
• A study on a Q-Learning algorithm application to a manufacturing assembly problem [0.8937905773981699]
  This study focuses on the implementation of a reinforcement learning algorithm in an assembly problem of a given object. A model-free Q-Learning algorithm is applied, considering the learning of a matrix of Q-values (Q-table) from the successive interactions with the environment. The optimisation approach achieved very promising results by learning the optimal assembly sequence 98.3% of the times.
  arXiv  Detail & Related papers  (2023-04-17T15:38:34Z)
• Learning MDPs from Features: Predict-Then-Optimize for Sequential Decision Problems by Reinforcement Learning [52.74071439183113]
  We study the predict-then-optimize framework in the context of sequential decision problems (formulated as MDPs) solved via reinforcement learning. Two significant computational challenges arise in applying decision-focused learning to MDPs.
  arXiv  Detail & Related papers  (2021-06-06T23:53:31Z)
• A Deep Reinforcement Learning Based Multi-Criteria Decision Support System for Textile Manufacturing Process Optimization [5.900286890213338]
  The present paper proposes a decision support system that combines the intelligent data-based random forest (RF) models and a human knowledge based analytical hierarchical process (AHP) structure. The effectiveness of this system has been validated in a case study of optimizing a textile ozonation process.
  arXiv  Detail & Related papers  (2020-12-29T15:09:48Z)
• Multi-Objective Optimization of the Textile Manufacturing Process Using Deep-Q-Network Based Multi-Agent Reinforcement Learning [5.900286890213338]
  The paper proposes a multi-agent reinforcement learning (MARL) framework to transform the optimization process into a game. A utilitarian selection mechanism was employed in the game to avoid the interruption of multiple equilibriumlibria. The proposed MARL system is possible to achieve the optimal solutions for the textile ozonation process and it performs better than the traditional approaches.
  arXiv  Detail & Related papers  (2020-12-02T11:37:44Z)
• Stein Variational Model Predictive Control [130.60527864489168]
  Decision making under uncertainty is critical to real-world, autonomous systems. Model Predictive Control (MPC) methods have demonstrated favorable performance in practice, but remain limited when dealing with complex distributions. We show that this framework leads to successful planning in challenging, non optimal control problems.
  arXiv  Detail & Related papers  (2020-11-15T22:36:59Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.