Reinforcement Learning Enables Resource Partitioning in Foraging Bats

Lesser long-nosed bats (Leptonycteris yerbabuenae) in the El Pinacate and Gran Desierto de Altar Biosphere Reserve employ a sophisticated reinforcement learning strategy to partition nectar resources efficiently. This approach, requiring minimal memory, allows them to create small, non-overlapping foraging cores without relying on social communication. Researchers tracked bat movements using GPS devices, aerial imaging, and video recordings, revealing that these bats navigate tens of kilometers daily from their roosts to cacti fields, where they compete for nectar. The study combines real data with evolutionary simulations to demonstrate how reinforcement learning enables optimal resource exploitation in this species.

Goldshtein, A., Handel, M., Eitan, O., Bonstein, A., Shaler, T., Collet, S., Greif, S., Medellín, R. A., Emek, Y., Korman, A., & Yovel, Y. (2020). Reinforcement Learning Enables Resource Partitioning in Foraging Bats. Current Biology, 30(20), 4096–4102. https://doi.org/10.1016/j.cub.2020.07.079