Deep reinforcement learning trains agents to track odor plumes with active sensing
Lawrence Jianqiao Hu, Elliott Abe, Harsha Gurnani, Daniel Sitonic, Floris van Breugel, Edgar Y. Walker, Bing Brunton · COSYNE 2025
The vast majority of natural animal behavior involves far more than passive perception and reflexive responses. Indeed, animals actively move themselves in the world and systematically collect sensory observations over time; such active sensing allows them to infer environmental states that are not directly measurable, which ultimately guide ethologically important behaviors. One clear example of such a behavior is odor plume localization in flying insects. By changing their speed and turning, insects produce variations in their multisensory inputs that, once integrated over time, can resolve
ePoster · Neuroscience