Understanding biological plume tracking behavior using deep reinforcement learning

The ability to track odor plumes in dynamic environments is critical for flying insects following attractive odors to localize food or mates. This remarkable tracking behavior requires multimodal integration of odor, vision, and wind sensing, is robust to variations in plume statistics and wind speeds, and can often be performed over large distances. Therefore, it is challenging to study in confined experimental settings. Here we describe ongoing work to explore the space of policies effective to accomplish plume tracking, leveraging the reproducibility and interpretability of artificial agents trained in biologically motivated simulations. Specifically, we trained neural-network (NN) agents with deep reinforcement learning to locate the source of a patchy simulated plume, while varying their capacity to store past sensory stimuli. We analyzed the behavior of trained agents by inspecting successful trajectories. We then …