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| Autores principales: | , , , |
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| Formato: | Preprint |
| Publicado: |
2026
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| Materias: | |
| Acceso en línea: | https://arxiv.org/abs/2605.15938 |
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| _version_ | 1866909046949281792 |
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| author | Rando, Marco Heinonen, Robin A. Qi, Yujia Seminara, Agnese |
| author_facet | Rando, Marco Heinonen, Robin A. Qi, Yujia Seminara, Agnese |
| contents | Finding an odor source in a turbulent flow requires effectively leveraging the history of olfactory observations into a robust navigation strategy. In this work, we use tabular Q-learning to train an olfactory search agent with a minimal memory of past observations: only a running clock since the last whiff. This agent learns an interpretable strategy to recover the plume which combines well-known behaviors observed in insects: surging, casting, and a return downwind. While achieving good performance on data from direct numerical simulations of turbulence, the agent is limited by an inability to adapt its strategy to the local intermittency level; we show that providing more flexibility improves robustness. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_15938 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Clock-state olfactory search in turbulent flows using Q-learning: The geometry of plume recovery Rando, Marco Heinonen, Robin A. Qi, Yujia Seminara, Agnese Biological Physics Machine Learning Finding an odor source in a turbulent flow requires effectively leveraging the history of olfactory observations into a robust navigation strategy. In this work, we use tabular Q-learning to train an olfactory search agent with a minimal memory of past observations: only a running clock since the last whiff. This agent learns an interpretable strategy to recover the plume which combines well-known behaviors observed in insects: surging, casting, and a return downwind. While achieving good performance on data from direct numerical simulations of turbulence, the agent is limited by an inability to adapt its strategy to the local intermittency level; we show that providing more flexibility improves robustness. |
| title | Clock-state olfactory search in turbulent flows using Q-learning: The geometry of plume recovery |
| topic | Biological Physics Machine Learning |
| url | https://arxiv.org/abs/2605.15938 |