Q-Learning算法在制造装配问题中的应用研究
A study on a Q-Learning algorithm application to a manufacturing assembly problem
AI总结：
本文研究将无模型Q-Learning算法应用于制造装配问题，通过Q-table学习最优装配序列，并在三个复杂度递增的场景中验证了其98.3%的最优序列学习成功率。
摘要：
机器学习算法的发展在应对制造决策问题日益增长的建模复杂性方面已变得愈发重要。强化学习是一种具有巨大潜力的方法，因为它对前期训练数据的需求较少，即系统在实际运行中随时间进行学习。本研究聚焦于在给定对象的装配问题中实现一种强化学习算法，旨在验证所提方法在优化装配过程时间方面的有效性。采用了一种无模型的Q-Learning算法，考虑从与环境的连续交互中学习Q值矩阵（Q-table），以给出装配序列解。该实现探索了三个复杂度递增的场景，以评估Q-Learning的参数和奖励的影响，从而提升强化学习智能体的性能。该优化方法在98.3%的情况下学习到了最优装配序列，取得了非常有前景的结果。
英文摘要：
The development of machine learning algorithms has been gathering relevance to address the increasing modelling complexity of manufacturing decision-making problems. Reinforcement learning is a methodology with great potential due to the reduced need for previous training data, i.e., the system learns along time with actual operation. This study focuses on the implementation of a reinforcement learning algorithm in an assembly problem of a given object, aiming to identify the effectiveness of the proposed approach in the optimisation of the assembly process time. 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 to suggest an assembly sequence solution. This implementation explores three scenarios with increasing complexity so that the impact of the Q-Learning\textsc's parameters and rewards is assessed to improve the reinforcement learning agent performance. The optimisation approach achieved very promising results by learning the optimal assembly sequence 98.3% of the times.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
