基于成对冲突的标准化特征提取在训练重调度问题中的应用
Standardized feature extraction from pairwise conflicts applied to the train rescheduling problem
摘要：
我们提出了一个训练重新调度算法，它适用于一个标准化的特征选择的基础上成对的冲突，以作为输入的强化学习框架。我们实现了一个分析方法，识别和最佳解决两列火车之间产生的每一个冲突，然后我们设计了相应的观察空间，考虑到这些冲突的最相关的信息。然后，以这种方式获得的数据转化为强化学习框架上下文中的动作。我们使用Flatland Challenge的评估指标来测试我们的初步模型。实证结果表明，建议的特征空间提供了有意义的意见，从中可以学习到一个明智的调度策略。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
