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Q-Learning to navigate turbulence without a map
By introducing a temporal memory, we demonstrate that two salient features of odor traces, discretized in few olfactory states, are sufficient to learn navigation in a realistic odor plume.
Physics Informed Shallow Machine Learning for Wind Speed Prediction
We train supervised learning algorithms using the past history of wind to predict its value at a future time (horizon).
Learning to predict target location with turbulent odor plumes
Animal behavior and neural recordings show that the brain is able to measure both the intensity of an odor and the timing of odor encounters.
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