{
  "abstract": "We study the run-tumble motion of a single autochemotactic agent that secretes and senses chemical cues using a reinforcement learning (RL) framework. In a chemical environment that changes dynamically via secretion, diffusion, and degradation of attractant chemicals, we study the effectiveness of RL for a successful navigation strategy. We show that RL enhances chemotactic performance. Starting from a random initial position, the probability of finding the agent in the favorable region (near the attractant peak) increases faster with time and reaches a higher value in the long time limit when RL is used, compared to the case when the agent performs usual run-tumble chemotaxis without using RL. We also characterize the chemotactic performance of the agent by varying different parameters like rates of secretion, degradation, and diffusion. Importantly, we observe a non-monotonic dependence of the chemotactic performance on the learning and exploration rates, indicating the existence of an optimal RL strategy for the most efficient navigation. We explain these observations as a result of the interplay between the pre-existing attractant environment and the agent-generated attractant profile.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": "Journal reference: Physical Review E 114, 054402 (2026)",
  "arxiv": "2611.07531",
  "author_total": 3,
  "authors": [
    {
      "name": "Ramesh Pramanik"
    },
    {
      "name": "Shradha Mishra"
    },
    {
      "name": "Sakuntala Chatterjee"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/fh995z32brcnk.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2611.07531",
  "fulltext_url": null,
  "id": "fh995z32brcnk",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2611.07531",
  "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",
      "cond-mat.stat-mech"
    ],
    [
      "arxiv",
      "q-bio.CB"
    ]
  ],
  "openalex": null,
  "pdf_url": "https://arxiv.org/pdf/2611.07531",
  "pmcid": null,
  "pmid": null,
  "published": "2026-11-05",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/fh995z32brcnk.ris",
  "sources": [
    {
      "first_seen": "2026-11-06T18:21:30Z",
      "native_id": "2611.07531",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2611.07531"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "arxiv:q-bio.CB",
      "subfield": "cell-biology"
    },
    {
      "field": "physics",
      "origin": "arxiv:cond-mat.stat-mech",
      "subfield": "statistical-nonlinear"
    }
  ],
  "title": "Autotaxis of a Run-and-Tumble Agent via Reinforcement Learning",
  "title_text": "Autotaxis of a Run-and-Tumble Agent via Reinforcement Learning",
  "type": "preprint",
  "updated_at": "2026-11-09T00:20:50Z",
  "url": "https://research.draconitic.com/paper/fh995z32brcnk",
  "venue": "arXiv",
  "versions": [
    {
      "doi": "10.1103/4xnp-dsn4",
      "pid": null,
      "relation": "published-as",
      "title": null,
      "venue": null
    }
  ]
}