Abstract: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.
| Comments: | 15 pages, 13 figures, 1 table |
| Subjects: | Biological Physics (physics.bio-ph); Machine Learning (cs.LG) |
| Cite as: | arXiv:2605.15938 [physics.bio-ph] |
| (or arXiv:2605.15938v1 [physics.bio-ph] for this version) | |
| https://doi.org/10.48550/arXiv.2605.15938 arXiv-issued DOI via DataCite (pending registration) |
From: Marco Rando [view email]
[v1]
Fri, 15 May 2026 13:19:26 UTC (4,645 KB)
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