Imagine you are surrounded by water. There is something you need to reach. You cannot really see your target, but you know it is moving, because your skin senses the currents it generates. You also know it cant be far, because you smell its odor. How can you reach your target? What are the algorithms that guide sensory driven navigation, in a world dominated by turbulence? Living systems solve this problem routinely to find food and mates or escape predators and many animals are exquisitely adapted to navigate turbulence. But what are the algorithms they use to:
combine prediction with navigation?
adapt to a changing physical environment
leverage multimodal sensory cues?
To tackle these questions we combine supervised learning with reinforcement learning using data from massive numerical simulations and asymptotic models of fluid dynamics.