{
  "abstract": "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.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2610.12177",
  "author_total": 1,
  "authors": [
    {
      "name": "Vladimir Parfenyev"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/d60znbqttpgze.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2610.12177",
  "fulltext_url": null,
  "id": "d60znbqttpgze",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2610.12177",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "http://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "physics.flu-dyn"
    ],
    [
      "openalex",
      "1702|Reinforcement Learning in Robotics"
    ]
  ],
  "openalex": "W7221182133",
  "pdf_url": "https://arxiv.org/pdf/2610.12177",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-08",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/d60znbqttpgze.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:27:54Z",
      "native_id": "2610.12177",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2610.12177"
    }
  ],
  "subjects": [
    {
      "field": "physics",
      "origin": "arxiv:physics.flu-dyn",
      "subfield": "fluid-dynamics"
    }
  ],
  "title": "Learning minimum-time navigation policies in two-dimensional flows with a genetic algorithm",
  "title_text": "Learning minimum-time navigation policies in two-dimensional flows with a genetic algorithm",
  "type": "preprint",
  "updated_at": "2026-10-30T12:27:54Z",
  "url": "https://research.draconitic.com/paper/d60znbqttpgze",
  "venue": "arXiv",
  "versions": []
}