本文介绍了一种增强学习方法，以更好地概括有关工作店调度问题（JSP）的启发式调度规则。 JSP上的当前模型并不关注概括，尽管正如我们在这项工作中所显示的那样，这是对问题进行更好的启发式方法的关键。改善概括的一种众所周知的技术是使用课程学习（CL）学习日益复杂的实例。但是，正如文献中许多作品所表明的那样，在不同问题大小之间传递学习技能时，这种技术可能会遭受灾难性的遗忘。为了解决这个问题，我们引入了一种新颖的对抗性课程学习（ACL）策略，该策略在学习过程中动态调整了难度级别以重新审视最坏情况的实例。这项工作还提出了一个深度学习模型来解决JSP，这是e var的W.R.T.作业定义和尺寸不可能。对Taillard和Demirkol的实例进行了实验，表明所提出的方法显着改善了JSP上的最新模型。它的平均最佳差距从Taillard的实例中的平均最佳差距从19.35 \％降低到10.46 \％，而Demirkol的实例中的平均最佳差距从38.43 \％降低到18.85％。我们的实施可在线提供。

Geologic cores are rock samples that are extracted from deep under the ground during the well drilling process. They are used for petroleum reservoirs' performance characterization. Traditionally, physical studies of cores are carried out by the means of manual time-consuming experiments. With the development of deep learning, scientists actively started working on developing machine-learning-based approaches to identify physical properties without any manual experiments. Several previous works used machine learning to determine the porosity and permeability of the rocks, but either method was inaccurate or computationally expensive. We are proposing to use self-supervised pretraining of the very small CNN-transformer-based model to predict the physical properties of the rocks with high accuracy in a time-efficient manner. We show that this technique prevents overfitting even for extremely small datasets. Github: https://github.com/Shahbozjon/porosity-and-permeability-prediction