This paper explores the combination of Reinforcement Learning (RL) and search-based path planners to speed up the optimization of flight paths for airliners, where in case of emergency a fast route re-calculation can be crucial. The fundamental idea is to train an RL Agent to pre-compute near-optimal paths based on location and atmospheric data and use those at runtime to constrain the underlying path planning solver and find a solution within a certain distance from the initial guess. The approach effectively reduces the size of the solver's search space, significantly speeding up route optimization. Although global optimality is not guaranteed, empirical results conducted with Airbus aircraft's performance models show that fuel consumption remains nearly identical to that of an unconstrained solver, with deviations typically within 1%. At the same time, computation speed can be improved by up to 50% as compared to using a conventional solver alone.

翻译：本文探讨了将强化学习与基于搜索的路径规划器相结合，以加速民航客机飞行路径的优化过程，在紧急情况下快速重新计算航线至关重要。其核心思想是训练一个强化学习智能体，使其能够基于位置与大气数据预计算接近最优的路径，并在运行时利用这些路径来约束底层路径规划求解器，从而在初始猜测的特定距离范围内找到解决方案。该方法有效缩小了求解器的搜索空间，显著提升了航线优化速度。尽管无法保证全局最优性，但基于空客飞机性能模型开展的实证结果表明，其燃油消耗量与无约束求解器所得结果几乎一致，偏差通常保持在1%以内。同时，与单独使用传统求解器相比，计算速度最高可提升50%。