چارچوب انطباق خود را برای خیاط کردن یک مدل یادگیری عصبی-عامل برای حل مشکلات زمانبندی پویا در زمان واقعی

The dynamic nature and time-varying behavior of actual environments provide serious challenges for learning models. Thus, changes may deteriorate the constructed control policy over time, which requires permanent adaptation strategies. Changes usually appear as an evolution in the relationship between instance variables composing stream data, known in machine learning under the term concept drift. Several adaptation strategies have been performed to tackle concept drifting data streams, always assuming that arrived instances are labeled, either completely or partially. However, this assumption is violated in many application areas, especially in the manufacturing field. We propose, in this paper, a new framework called Labeling Extraction from the current Model (LEM). LEM is adapted to retrieve learning labels, relying uniquely on unlabeled received instances and without any external supervision, which has never been previously addressed. Hence, to the best of our knowledge, there has been no effort addressing scheduling manufacturing problems for adaptation to data streams with concept drifts. Experiments are conducted to show the effectiveness of LEM. The obtained results demonstrate the ability of LEM to maintain the stability and efficiency of the control policy approximated by the learning model, by significantly improving its prediction performance, compared to its use without adaptation.