The design and planning of railway alignments is the dominant task in railway construction. However, it is difficult to achieve self-learning and learning from human experience with manual as well as automated design methods. Also, many existing approaches require predefined numbers of horizontal points of intersection or vertical points of intersection as input. To address these issues, this study employs deep reinforcement learning (DRL) to optimize mountainous railway alignments with the goal of minimizing construction costs. First, in the DRL model, the state of the railway alignment optimization environment is determined, and the action and reward function of the optimization agent are defined along with the corresponding alignment constraints. Second, we integrate a recent DRL algorithm called the deep deterministic policy gradient with optional human experience to obtain the final optimized railway alignment, and the influence of human experience is demonstrated through a sensitivity analysis. Finally, this methodology is applied to a real-world case study in a mountainous region, and the results verify that the DRL approach used here can automatically explore and optimize the railway alignment, decreasing the construction cost by 17.65% and 7.98%, compared with the manual alignment and with the results of a method based on the distance transform, respectively, while satisfying various alignment constraints.

中文翻译：

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山地铁路路线优化的深度强化学习方法

铁路线形设计和规划是铁路建设的主导任务。然而，手动和自动设计方法很难实现自学习和从人类经验中学习。此外，许多现有方法需要预定义数量的水平交点或垂直交点作为输入。为了解决这些问题，本研究采用深度强化学习 (DRL) 来优化山区铁路路线，目标是最大限度地降低建设成本。首先，在 DRL 模型中，确定了铁路线形优化环境的状态，并定义了优化代理的动作和奖励函数以及相应的线形约束。第二，我们将最新的称为深度确定性策略梯度的 DRL 算法与可选的人类经验相结合，以获得最终优化的铁路路线，并通过敏感性分析证明人类经验的影响。最后，将该方法应用于山区的实际案例研究，结果验证了此处使用的 DRL 方法可以自动探索和优化铁路线形，相比之下，施工成本降低了 17.65% 和 7.98%。手动对齐和基于距离变换的方法的结果，同时满足各种对齐约束。