论文标题
从对火车重新安排问题的成对冲突中提取的标准化特征提取
Standardized feature extraction from pairwise conflicts applied to the train rescheduling problem
论文作者
论文摘要
我们提出了一种重新安排算法的火车，该算法基于成对冲突应用标准化的特征选择，以作为加固学习框架的输入。我们实施了一种分析方法，该方法可以识别并最佳地解决两列火车之间产生的所有冲突，然后我们设计了一个相应的观察空间，该空间具有考虑这些冲突的最相关信息。以这种方式获得的数据然后转化为在增强学习框架上下文中的动作。我们使用Flatland Challenge的评估指标测试我们的初步模型。经验结果表明，建议的特征空间提供了有意义的观察，可以从中学习明智的调度策略。
We propose a train rescheduling algorithm which applies a standardized feature selection based on pairwise conflicts in order to serve as input for the reinforcement learning framework. We implement an analytical method which identifies and optimally solves every conflict arising between two trains, then we design a corresponding observation space which features the most relevant information considering these conflicts. The data obtained this way then translates to actions in the context of the reinforcement learning framework. We test our preliminary model using the evaluation metrics of the Flatland Challenge. The empirical results indicate that the suggested feature space provides meaningful observations, from which a sensible scheduling policy can be learned.
