pith:W35LIN2S
Clock-state olfactory search in turbulent flows using Q-learning: The geometry of plume recovery
A running clock since the last odor whiff lets a Q-learning agent learn surging, casting and downwind return to recover plumes in turbulence.
arxiv:2605.15938 v1 · 2026-05-15 · physics.bio-ph · cs.LG
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\pithnumber{W35LIN2S3RC4V4VLOIIVC3UJAV}
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Claims
The 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.
That a single running clock since the last whiff supplies enough state information for tabular Q-learning to converge on a robust recovery policy across varying turbulence intermittency levels.
A tabular Q-learning agent with clock-state memory learns surging, casting, and downwind return to recover odor plumes in turbulent flows from direct numerical simulations.
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| First computed | 2026-05-20T00:01:45.827183Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
b6fab43752dc45caf2ab7211516e89055f5f55103f1c0f829a874d8db1ccff45
Aliases
· · · · ·Agent API
Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/W35LIN2S3RC4V4VLOIIVC3UJAV \
| jq -c '.canonical_record' \
| python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: b6fab43752dc45caf2ab7211516e89055f5f55103f1c0f829a874d8db1ccff45
Canonical record JSON
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