A study on a Q-Learning algorithm application to a manufacturing assembly problem

• URL: http://arxiv.org/abs/2304.08375v1
• Date: Mon, 17 Apr 2023 15:38:34 GMT
• Title: A study on a Q-Learning algorithm application to a manufacturing assembly problem
• Authors: Miguel Neves, Miguel Vieira, Pedro Neto
• Abstract summary: 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.
• Score: 0.8937905773981699
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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