Research on Optimal Takeoff and Landing Windows of Airport Runways Based on Reinforcement Learning

TANG Xinke; ZHANG Duo; ZHANG Kun

doi:10.12382/bgxb.2025.1032

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Research on Optimal Takeoff and Landing Windows of Airport Runways Based on Reinforcement Learning

更新时间：2026-04-20

- Research on Optimal Takeoff and Landing Windows of Airport Runways Based on Reinforcement Learning
- Acta Armamentarii (2026)
- 作者机构：
  
  国防科技大学理学院，湖南长沙410073、TakeoffandLandingWindow，TLW
- 作者简介：
- 基金信息：
- DOI：10.12382/bgxb.2025.1032
  CLC： O38
- Received：24 November 2025，
  
  Online First：20 April 2026，
- 稿件说明：
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唐欣珂，张舵，张昆. 基于强化学习方法的机场跑道最优起降窗口研究[J/OL]. 兵工学报, 2026(2026-04-20). https://doi.org/10.12382/bgxb.2025.1032.

TANG X K, ZHANG D, ZHANG K. Research on optimal takeoff and landing windows of airport runways based on reinforcement learning[J/OL]. Acta Armamentarii, 2026(2026-04-20). https://doi.org/10.12382/bgxb.2025.1032. (in Chinese)
唐欣珂，张舵，张昆. 基于强化学习方法的机场跑道最优起降窗口研究[J/OL]. 兵工学报, 2026(2026-04-20). https://doi.org/10.12382/bgxb.2025.1032. DOI：

TANG X K, ZHANG D, ZHANG K. Research on optimal takeoff and landing windows of airport runways based on reinforcement learning[J/OL]. Acta Armamentarii, 2026(2026-04-20). https://doi.org/10.12382/bgxb.2025.1032. (in Chinese) DOI：

摘要

机场跑道封锁是毁伤评估领域的重要研究方向，其核心在于依据起降窗口(Takeoff and Landing Window，TLW)判据高效定位不同毁伤场景下的最优TLW，而经典遍历算法虽可通过遍历离散位置与角度获取全局最优解，但其精度与效率均受离散尺寸制约。引入强化学习算法，通过构建考虑弹坑分布与跑道几何约束的状态-动作空间，并设计分段多层奖励函数，采用近端策略优化算法实现智能体的策略训练。对比结果表明，在多种毁伤度场景下，强化学习方法在连续跑道空间内获得的TLW长度与遍历算法最优解的误差均不超过0.49%，角度偏差不大于1.1°，展现出良好的鲁棒性与精度。通过奖励函数对比与训练曲线分析证明，所提出的分段多层奖励模型相较于单值基础奖励模型，能够显著提升复杂场景下的精度与稳定性。研究结果表明，强化学习方法在机场跑道封锁毁伤问题研究中具有重要价值。

Abstract

Airport runway blockade is an important research direction in the field of damage assessment. Its core lies in efficiently locating the optimal Takeoff and Landing Window (TLW) under different damage scenarios according to TLW criteria. Although classical traversal algorithms can obtain the global optimal solution by traversing discrete positions and angles

their accuracy and efficiency are both restricted by the discrete size.This paper introduces a Reinforcement Learning (RL) algorithm

which constructs a state-action space model considering crater distribution and runway geometric constraints

designs a Multi-Level Reward Function (MRF)

and adopts the Proximal Policy Optimization (PPO) algorithm to implement policy training. Comparison results show that under various damage degree scenarios

the error between the TLW length obtained by the RL method in the continuous runway space and the optimal solution of the ES algorithm is no more than 0.5%

and the angular deviation is no more than 1.1°

exhibiting excellent robustness and accuracy. Comparisons of reward functions and analysis of training curves demonstrate that the proposed MRF can significantly improve the policy convergence speed and stability compared with the Baseline Reward Function (BRF). The research results indicate that RL has important value in the research on airfield runway closure damage assessment.

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references

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