..., Reinforcement Learning (RL) shows better adaptability to complex and dynamic working environments. However, the training of RL models is often time-consuming, and the determination of well-behaved reward function parameters is challenging. To tackle these issues, an adaptive robot motion planning approach...

... technology is already established and will be used as intelligent control by integrating a deep reinforcement learning (deep RL) approach [ 11 , 12 ]. The method has a significant potential for a range of applications in space and on Earth. The use of noncontact, minimally invasive, and flexibly...

... the training of an adaptive scheduler utilizing graph neural networks and reinforcement learning. Furthermore, a simulator is developed to simulate interruption, swapping, and blocking in the ISBJSSP setting. By employing a set of reported benchmark instances, we conduct a detailed experimental study...

... , Y. , Tan , J. , and Wu , F. , 2020 , “ Petri-Net-Based Dynamic Scheduling of Flexible Manufacturing System via Deep Reinforcement Learning With Graph Convolutional Network ,” J. Manuf. Syst. , 55 , pp. 1 – 14 . 10.1016/j.jmsy.2020.02.004 [8] Ayvaz , S. , and Alpay , K...