Imprinting in Autonomous Artificial Agents Using Deep Reinforcement Learning

Abstract

Imprinting is a common survival strategy in which an animal learns a lasting preference for its parents and siblings early in life. To date, however, the origins and computational foundations of imprinting have not been formally established. What learning mechanisms generate imprinting behavior in newborn animals? Here, we used deep reinforcement learning and intrinsic motivation (curiosity), two learning mechanisms deeply rooted in psychology and neuroscience, to build autonomous artificial agents that imprint. When we raised our artificial agents together in the same environment, akin to the early social experiences of newborn animals, the agents spontaneously developed imprinting behavior. Our results provide a pixels-to-actions computational model of animal imprinting. We show that domain-general learning mechanisms—deep reinforcement learning and intrinsic motivation—are sufficient for embodied agents to rapidly learn core social behaviors from unsupervised natural experience.