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.
Optimal trajectories for Bayesian olfactory search in turbulent flows: The low information limit and beyond.Physical review fluids, 10(4):044601, 2025
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Clock-state olfactory search in turbulent flows using Q-learning: The geometry of plume recovery
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.