发表机构
Landau Institute for Theoretical Physics of the Russian Academy of Sciences; HSE University（俄罗斯科学院朗道理论物理研究所; 高等经济学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究针对二维流场中载体的最短时间导航问题，采用遗传算法优化神经网络参数化的导航策略，其性能优于Q学习等方法，且策略具有鲁棒性，为复杂流场最短时间轨迹计算提供高效替代方案。
AI 中文摘要
我们研究已知二维流体流场中，具有固定大小滑移速度和可控方向的载体在两点间的最短时间导航问题。导航策略由神经网络参数化，并使用遗传算法优化，该算法通过达尔文选择和参数变异（无交叉）演化候选策略集合。该方法在包括二维湍流在内的多个基准案例中恢复了解析和数值最优控制解，且性能优于Q学习和单步演员-评论算法。我们进一步表明，只要未解析的小尺度湍流波动的特征速度远小于载体的滑移速度，学习到的导航策略对起始位置变化和未解析小尺度湍流波动具有鲁棒性。所提方法提供了导航策略的紧凑表示，易于并行化，且无需奖励塑形，为计算复杂流场中最短时间轨迹的现有解析和数值方法提供了高效替代方案。
英文摘要
We study minimum-time navigation between two points in a known two-dimensional fluid flow for a vessel with fixed-magnitude slip velocity and controllable direction. The navigation policy is parameterized by a neural network and optimized using a genetic algorithm that evolves an ensemble of candidate strategies through Darwinian selection and parameter mutation, without crossover. The method recovers analytical and numerical optimal-control solutions in several benchmark cases, including two-dimensional turbulence, and outperforms Q-learning and one-step actor-critic method. We further show that the learned navigation strategies are robust to variations in the starting position and to unresolved small-scale turbulent fluctuations, provided that the characteristic velocity of these fluctuations remains small compared to the vessel's slip velocity. The proposed approach provides a compact representation of the navigation policy, is readily parallelizable, and eliminates the need for reward shaping, offering an efficient alternative to existing analytical and numerical methods for computing minimum-time trajectories in complex flows.
