Recovery Strategy Geometry

How it works

Highlighted claims

• All trained agents exhibit a consistent three-phase sequence after plume loss: surge upwind, lateral casting with oscillating forward motion, then downwind return. — Clock-state olfactory search in turbulent flows using Q-learning: The geometry of plume recovery
• Surge length increases strongly and monotonically with plume sparsity because longer inter-detection intervals mean the clock at plume loss encodes more information about distance from the last known plume position. — Clock-state olfactory search in turbulent flows using Q-learning: The geometry of plume recovery
• Dense plumes require early downwind returns to prevent overshooting, while sparse plumes demand prolonged upwind search to avoid entrapment, placing the two imperatives in direct conflict. — Clock-state olfactory search in turbulent flows using Q-learning: The geometry of plume recovery
• The surge-cast-return structure emerges naturally from optimization under turbulent plume statistics without being explicitly programmed. — Clock-state olfactory search in turbulent flows using Q-learning: The geometry of plume recovery
• Cast width shows no strong monotone trend with sparsity; wind speed is identified as a primary determinant of cast width and downwind return extent. — Clock-state olfactory search in turbulent flows using Q-learning: The geometry of plume recovery